This directory is created automatically and some papers may be mislabeled. Only document within the CiteSeer database are listed. The directory is intended to provide entry points for browsing the database and is not intended to be authoritative. Papers may not appear in all relevant categories. For example, papers in a sub-category may not appear in higher level categories.
21931.7 On Growing Better Decision Trees from Data - Murthy (1997)(Correct)
This thesis investigates the problem of growing decision trees from data, for the purposes
of classification and prediction.
After a comprehensive, multi-disciplinary survey of work on decision trees,... / context of decision tree or rule induction. With the rapid br in the context of rule induction Their conclusions are
8321.7 Processing Swedish Sentences: A Unification-Based Grammar and Some.. - Gambäck (1997)(Correct)
A unification-based grammar is a type of language description well suited for the implementation
on a computer. As many other contemporary grammar theories, almost all the
unification-based ones advoc... / retrieval grammar and transfer-rule induction etc.have certainly not been
7090.0 Machine Learning and Natural Language Processing - Marquez (2000)(Correct)
In this report, some collaborative work between the fields of Machine Learning (ML) and Natural Language Processing (NLP) is presented. The document is structured in two parts. The first part includes... / instance-based learning rule-induction systems etc. We br are other popular logic-based rule-induction systems that employ different
5655.6 Goal-Driven Learning - Ram, Leake (1995)(Correct)
Contents
Acknowledgements
Preface by Professor Tom Mitchell, CMU
Editors' Preface
List of Contributors
1. Learning, Goals, and Learning Goals : : : : : : : : : : : : : : : : : : : : : : : : : : : : :... / of Theory and Similarity in Rule Induction br of Theory and Similarity in Rule Induction is reprinted from D. Fisher
5648.5 Learning Effective And Robust Knowledge For Semantic Query.. - Hsu (1997)(Correct)
xi
1 Introduction 1
1.1 Semantic Query Optimization : : : : : : : : : : : : : : : : : : : : : : 3
1.2 High Utility Semantic Knowledge for SQO : : : : : : : : : : : : : : : 6
1.3 Learning Effective an... / . . Related Work in Rule Induction and Data Mining br and cost-reduction in rule induction to learn effective rules. The
5108.8 Symbol Grounding: A New Look At An Old Idea - Sun (1999)(Correct)
Symbols should be grounded, as has been argued before. But we insist that they should be
grounded not only in subsymbolic activities, but also in the interaction between the agent and the
world. The... /
5000.8 Using Inductive Logic Programming to Automate the Construction of.. - Zelle (1995)(Correct)
vii
Chapter 1 Introduction 1
1.1 Empirical NLP : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2
1.2 CHILL: An Empirical Parser Acquisition System : : : : : : : : : : : 4
1.3 Organization... / . . Control-Rule Induction br Example Analysis Control Rule Induction Program Specialization
4878.1 Using Multi-Strategy Learning to Improve Planning Efficiency and.. - Estlin (1998)(Correct)
viii
Chapter 1 Introduction 1
1.1 Acquiring Planning Control Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Scope: A Control Knowledge Acquisition System . . . . . . . . . . . . . ... / . . Control Rule Induction . br is then passed to the control rule induction phase where it is used to
4408.1 Practical Uses of the Minimum Description Length Principle in.. - Pfahringer (1995)(Correct)
This thesis tackles a very basic Machine Learning problem: given a few alternative
hypotheses, each more or less complex and each covering the training examples
to a greater or lesser extent, decide w... / Contents Introduction Rule Induction in Knopf . br Machine Learning. Chapter Rule Induction in Knopf .
4246.3 Feature Subset Selection Using A Genetic Algorithm - Yang, Honavar (1997)(Correct)
Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This is... / Richeldi and Lanzi or rule induction systems Vafaie and De Jong
4095.3 A Framework for Goal-Driven Learning - Ashwin Ram(Correct)
this paper, we describe
a framework for goal-driven learning and its relationship
to prior and current theories from each of these
perspectives. unknown Proceedings of the 1994 AAAI Spring Symposium o... / of Theory and Similarity in Rule Induction. In D. Fisher M.J. Pazzani
4031.5 Unifying Instance-Based and Rule-Based Induction - Domingos (1996)(Correct)
Several well-developed approaches to inductive learning now exist, but each has specific limitations that are hard to overcome. Multi-strategy learning attempts to tackle this problem by combining m... / empirical approaches rule induction and instance-based learning. br multi-strategy learning rule induction instance-based learning
3797.2 Bayesian Integration of Rule Models - Pedro Domingos(Correct)
Although Bayesian model averaging (BMA) is in principle the optimal method
for combining learned models, it has received relatively little attention in the machine
learning literature. This article de... / of the application of BMA to rule induction. BMA is applied to a variety br Bayesian model averaging rule induction classification overfitting
3406.8 Intelligent Data Analysis in Medicine - Lavrac, Keravnou, Zupan (2000)(Correct)
Extensive amounts of knowledge and data stored in medical databases require the
development of specialized tools for storing and accessing of data, data analysis, and effective
use of stored knowled... / methods . Rule induction . br of unclassified examples. . Rule induction . . If-then rules
3341.9 From Implicit Skills to Explicit Knowledge: A Bottom-Up Model of.. - Sun, Merrill, Peterson (1999)(Correct)
This paper presents a skill learning model Clarion. Di erent from existing models of mostly high-level
skill learning that use a top-down approach (that is, turning declarative knowledge into procedur... / Learning with Adaptive Rule Induction ON-line is as follows . br learning and rule induction and exploits synergy of the
3329.1 Knowledge Representation in Machine Learning - Clark (1989)(Correct)
Introduction
Knowledge representation is a topic poorly discussed in machine learning. However, it is
perhaps the fundamental consideration in the design of any learning system, because the
represent... / Representational Systems . Rule Induction from Examples Learning can br is sometimes referred to as rule induction from examples'Despite its
3291.8 Program Derivation by Proof Transformation - Anderson (1993)(Correct)
In the proofs-as-programs methodology, verified programs are developed through theorem-proving in a constructive logic. Under this approach, the theorem-proving process can be regarded as a program de... / extraction simplification induction rule br extraction simplification induction rule The elimination rules for
3270.4 Data Mining in Medicine: Selected Techniques and Applications - Lavrac (1998)(Correct)
Widespread use of medical information systems
and explosive growth of medical databases require traditional
manual data analysis to be coupled with methods for efficient
computer-assisted analysis. ... / generalize the training cases rule induction and decision tree induction br rest of this section. . Rule induction Given a set of classified
3251.5 Integrating Explanation-Based and Inductive Learning Techniques to.. - Estlin (1996)(Correct)
Planning systems have become an important tool for automating a wide variety
of tasks. Control knowledge guides a planner to find solutions quickly and is crucial
for efficient planning in most domain... / is then used in the control rule induction phase to generate br Training Examples Control Rule Induction Program Specialization
3149.5 Autonomous Learning of Sequential Tasks: Experiments and Analyses - Ron Sun (1998)(Correct)
This paper presents a novel learning model Clarion, which is a hybrid model based on the twolevel
approach proposed in Sun (1995). The model integrates neural, reinforcement, and symbolic
learning m... / Learning with Adaptive Rule Induction ON-line which is similar to br sizes. At the top level for rule induction updating all the relevant
3117.7 A Comparison of Prediction Accuracy, Complexity, and Training Time of .. - Lim, Loh, Shih (1999)(Correct)
Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classification accuracy, training time, and (in the case of trees) number ... / tree is constructed the C . rule induction program is used to produce a br W. W. Fast effective rule induction in A. Prieditis and S.
3061.4 Augmented Bibliography - Appendix Augmented(Correct)
A discussion is given of a number of expert systems in the library and information
field, and the author proceeds to consider expert systems development in the business, industry
and government area... / combinations of expert systems rule induction fuzzy logic neural networks
2937.6 Methods and problems in data mining - Mannila (1997)(Correct)
Knowledge discovery in databases and data mining aim at semiautomatic tools for the analysis of large data sets. We consider some methods used in data mining, concentrating on levelwise search for all... /
2825.1 An Investigation of Hybrid Systems for Reasoning in Noisy Domains - Melvin (1995)(Correct)
This thesis discusses aspects of design, implementation and theory of expert systems,
which have been constructed in a novel way using techniques derived from
several existing areas of Artificial Inte... / Ai Techniques The Induction Rules Described Above. A More
2807.5 Inductive Theorem Proving in Theories Specified by.. - Wirth, Kühler (1995)(Correct)
We present an inference system for clausal theorem proving w.r.t. various kinds of inductive
validity in theories specified by constructor-based positive/negative-conditional equations. The reductio... / step applying a so-called induction rule. Besides generating br given by an application of the induction rule. Since Bachmair
2803.1 The RISE 2.0 System: A Case Study in Multistrategy Learning - Domingos (1995)(Correct)
Several well-developed approaches to inductive learning now exist, but each
has specific limitations that are hard to overcome. Multi-strategy learning attempts
to tackle this problem by combining mul... / empirical approaches rule induction and instance-based learning. br multi-strategy learning rule induction instance-based learning
2718.4 A Hybrid Agent Architecture For Reactive Sequential Decision Making - Sun, Peterson (1997)(Correct)
INTRODUCTION
How does an autonomous agent that interacts with an environment learn to
survive in the environment and make the most out of it? More specifically,
how can it develop a set of coping ski... / Learning with Adaptive Rule Induction ON-line. It consists of two br learning algorithm and the rule induction algorithm can work together
2700.7 A Comparison of Rule and Exemplar-Based Learning Systems - Clark (1980)(Correct)
Recently, there has been renewed interest in the use of exemplar-based schemes
for concept representation and learning. In this paper, we compare systems learning
concepts represented in this form wit... / rules such as the ID and AQ rule induction systems. We aim to clarify br and two families of rule induction system based on the AQ
2667.6 Discovering Robust Knowledge from Databases that Change - Hsu, Knoblock(Correct)
Many applications of knowledge discovery and data mining such as rule discovery
for semantic query optimization, database integration and decision support, require
the knowledge to be consistent with ... / be applied on top of other rule induction and data mining systems to br Previous work in classification rule induction Cohen Cohen
2658.4 Fundamentals Of Deductive Program Synthesis - Manna, Waldinger (1992)(Correct)
An informal tutorial is presented for program synthesis, with an emphasis on deductive methods. According to this approach, to construct a program meeting a given specification, we prove the existence... / equality and a well-founded induction rule. INTRODUCTION This is an br term. ffl Mathematical induction rule. Assumes that the desired
2641.0 Partial Computations in Constructive Type Theory - Smith (1991)(Correct)
Constructive type theory as conceived by Per Martin-Lof has a very
rich type system, but partial functions cannot be typed. This also
makes it impossible to directly write recursive programs. In thi... / a Scott-style fixed point induction rule allows for direct inductive br can be derived from the induction rule given below let P x be
2624.1 Machine Learning: Techniques and Recent Developments - Clark (1990)(Correct)
The use of expert systems is becoming more and more widespread, making the
need for appropriate machine learning techniques more acute to help ease the knowledge
aquisition bottleneck. Additionally, t... / with a particular emphasis on rule induction techniques. Firstly we provide br provide a summary of existing rule induction techniques including
2591.9 ILA-2: An Inductive Learning Algorithm over uncertain data - Tolun, Sever, al.(Correct)
ABSTRACT AND CONCLUSION NEEDS TO BE RE-WRITTEN. ESPECIALLY WE SHOULD
EMPHASIZE OUR CONTRIBUTION AND ORGINALITY OF THE WORK IN CONCLUSION.
In this paper we describe the ILA-2 rule induction algorithm f... / paper we describe the ILA- rule induction algorithm from the machine br Learning Inductive Learning Rule Induction. . Introduction A
2529.0 Context-Sensitive Feature Selection for Lazy Learners - Domingos (1997)(Correct)
High sensitivity to irrelevant features is arguably the main shortcoming of simple lazy learners. In response to it, many feature selection methods have been proposed, including forward sequential sel... / Quinlan and rule induction Clark Niblett They br Quinlan and rule induction algorithms Clark Niblett
2510.7 Rule Induction with Extension Matrices - Wu(Correct)
This paper presents a heuristic, attribute-based, noise-tolerant data mining program,
HCV (Version 2.0), based on the newly-developed extension matrix approach. By dividing
the positive examples (PE) ... / Rule Induction with Extension Matrices br Attributes In the context of rule induction and decision tree
2441.0 Learning Bias and Phonological Rule Induction - Gildea, Jurafsky (1996)(Correct)
this paper we suggest that an alternative to the purely nativist or purely empiricist
learning paradigms is to represent the prior knowledge of language as a set of abstract learning
biases, which gui... / Learning Bias and Phonological Rule Induction Daniel Gildea Daniel br Learning BiasandPhonological RuleInduction to as Universal Grammar
2417.8 Neuralbase: A Neural Network System For Case Based Retrieval In The.. - Gledhill (1995)(Correct)
The corporate computer help desk is becoming an increasingly difficult place to work due to
the variety of hardware, software and networking facilities now available. Automation of the
help desk can a... / . . . . Rule Induction br factors. . . . . Rule Induction As well as being acquired
2317.9 A Process-Oriented Heuristic for Model Selection - Pedro Domingos (1998)(Correct)
Current methods to avoid overfitting are either
data-oriented (using separate data for
validation) or representation-oriented (penalizing
complexity in the model). This paper
proposes process-oriented... / it to one type of learning rule induction. A process-oriented version br separate and conquer rule induction process Clark Niblett
2154.0 Towards a Meta-Theory of Operational Semantics - Wilson (1992)(Correct)
Introduction
There are several ways to give a semantics of a programming language. Each kind of semantics gives a
different insight into the meaning of a language, and is useful for different tasks.
... / we can use the technique of rule induction to prove assertions about
2140.2 Rule Induction and Instance-Based Learning: A Unified Approach - Domingos (1995)(Correct)
This paper presents a new approach to inductive learning that combines aspects of instancebased learning and rule induction in a single simple algorithm. The RISE system searches for rules in a specif... / Rule Induction and Instance-Based Learning A br of instancebased learning and rule induction in a single simple algorithm.
2120.4 Planning Proofs of Equations in CCS - Monroy, Bundy, Green (1999)(Correct)
Most e orts to automate formal veri cation of communicating systems have centred
around nite-state systems (FSSs). However, FSSs are incapable of modelling many practical
communicating systems, inc... / used in the selection of the induction rule and variable If the use of
2115.5 Enhanced hypertext categorization using hyperlinks - Chakrabarti, Dom, Indyk (1998)(Correct)
A major challenge in indexing unstructured hypertext databases
is to automatically extract meta-data that enables
structured search using topic taxonomies, circumvents keyword
ambiguity, and improves ... / dataset classifiers based on rule induction or feature selection classify
2010.9 Heterogeneous Uncertainty Sampling for Supervised Learning - Lewis, Catlett (1994)(Correct)
Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of ... / for training another the C . rule induction program Despite being br our current decision rule induction software cannot practicably be
1998.8 Process-Oriented Estimation of Generalization Error - Domingos (1999)(Correct)
Methods to avoid over tting fall into two broad
categories: data-oriented (using separate data
for validation) and representation-oriented (penalizing
complexity in the model). Both have
limitations t... / and successfully applied it to rule induction Domingos b br the model Application to Rule Induction Most rule induction systems
1989.1 Estimating Continuous Distributions in Bayesian Classifiers - John, Langley (1995)(Correct)
When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing,... / comparable to those for rule-induction methods in medical domains
1977.4 Erratic Fudgets: A Semantic Theory for an Embedded Coordination.. - Moran, Sands, Carlsson (1999)(Correct)
The powerful abstraction mechanisms of functional programming
languages provide the means to develop domain-specific programming
languages within the language itself. Typically, this is realised by
... / of the unique fixed-point induction rule. Section deals with the br to the denotational fixed-point induction rule which characterises a
1973.0 Data mining: machine learning, statistics, and databases - Mannila (1996)(Correct)
Knowledge discovery in databases and data mining
aim at semiautomatic tools for the analysis of
large data sets. We give an overview of the area and
present some of the research issues, especially fr... / decision tree learning or rule induction is one of the main components
1965.8 Behavioral Subtyping Using Invariants and Constraints - Liskov, Wing (1999)(Correct)
We present a way of defining the subtype relation that ensures that subtype objects preserve behavioral
properties of their supertypes. The subtype relation is based on the specifications of the sub- ... / discard the standard data type induction rule we prohibit the use of an br discard the standard data type induction rule we prohibit the use of an
1955.4 A mathematical theory of learning - Rickert (1998)(Correct)
ABSTRACT . Discussions of the role of mathematics in cognition typically emphasize the application
of discrete logic to internal symbolic representations. The traditional assumption is that learning i... / described as performing rule induction or discovering rules which
1921.6 Rapid Development of NLP Modules with Memory-Based Learning - Daelemans, van den Bosch, Zavrel.. (1998)(Correct)
The need for software modules performing natural language processing (NLP) tasks is growing. These modules should perform efficiently and accurately, while at the same time rapid development is often ... / earlier experiences as in rule induction and rule-based processing br experiences as in rule induction and rule-based processing The
1921.1 Descente Infinie + Deduction - Wirth (2000)(Correct)
Although induction is omnipresent, inductive theorem proving in the form of descente
infinie has not yet been integrated into full first-order deductive calculi. We present a solution
based on lemma... / inference rule then this induction rule can be just added to the br is not possible with the induction rules of Baaz al. In our
1887.9 Applying Neural Networks - Stader (1992)(Correct)
The claims made about neural networks' strengths and weaknesses vary. This
report intends to put these claims into perspective. There is a wealth of connectionist
methods and techniques each suited to... /
1882.1 Feature Subset Selection for Rule Induction Using RIPPER - Yang, Tiyyagura, al.(Correct)
The choice of features or attributes used to
represent patterns in the synthesis of pattern
classifiers using machine learning algorithms
has a strong impact on the accuracy of the
classifier, the num... / Feature Subset Selection for Rule Induction Using RIPPER Jihoon Yang br the proposed hybrid approach to rule induction an attractive approach to
1878.9 Finite Axiom Systems for Testing Preorder and De Simone Process.. - Irek Ulidowski (1996)(Correct)
We prove that testing preorder of De Nicola and Hennessy is preserved by all De
Simone process operators. Based on this result we propose an algorithm for generating
axiomatisations of testing preorde... / we use one infinitary induction rule. The usefulness of our results br system with one infinitary induction rule. We start by recalling the
1863.7 Empirical Learning of Natural Language Processing Tasks - Daelemans, van den Bosch, Weijters (1997)(Correct)
Language learning has thus far not been a hot application for machine-learning
(ML) research. This limited attention for work on empirical learning of language
knowledge and behaviour from text and sp... / empirical ML methods such as rule induction top down induction of br viz. decision-tree learning and rule induction section artificial neural
1782.7 A New Approach for Induction: From a Non-Axiomatic Logical Point of.. - Wang (1995)(Correct)
Non-Axiomatic Reasoning System (NARS) is designed to be a general-purpose intelligent
reasoning system, which is adaptive and works under insufficient knowledge
and resources. This paper focuses on th... / types of uncertainty. An induction rule generates conclusions from br kind of plant Formally the induction rule of NARS looks like this M
1777.1 Extracting Hidden Context - Michael Harries (1998)(Correct)
Concept drift due to hidden changes in context complicates learning
in many domains including financial prediction, medical diagnosis, and
network performance. Existing machine learning approaches to ... / decision tree algorithms rule induction algorithms and ILP
1717.7 Instance-Based Learning: Nearest Neighbour with Generalisation - Martin (1995)(Correct)
Instance-based learning is a machine learning method that classifies new examples by
comparing them to those already seen and in memory. There are two types of
instance-based learning; nearest neighbo... / nested generalised exemplars rule induction methods and a composite rule br methods and a composite rule induction and nearest neighbour learner.
1714.9 Set Theory for Verification: II - Induction and Recursion - Paulson (1995)(Correct)
A theory of recursive definitions has been mechanized in Isabelle's Zermelo-Fraenkel
(ZF) set theory. The objective is to support the formalization of particular recursive definitions
for use in ver... / Inference System in ZF . Rule Induction . Proving the Soundness br prop H p oe q . Rule Induction Because it is defined using
1701.1 Abstraction Considered Harmful: Lazy Learning Of Language Processing - Walter Daelemans (1996)(Correct)
ION CONSIDERED HARMFUL: LAZY
LEARNING OF LANGUAGE PROCESSING
Walter Daelemans
Computational Linguistics Tilburg University, The Netherlands,
and Center for Dutch Language and Speech, University of An... / induction of decision trees rule induction methods and supervised br systems hand made or using rule-induction and statistical systems
1693.3 Feature Engineering for a Symbolic Approach to Text Classification - Scott (1998)(Correct)
Most text classification research to date has used the standard "bag of words"
model for text representation inherited from the word-based indexing techniques
used in information retrieval research.... / . Rule Induction Vs. Statistical Pattern br improvement over previous rule induction algorithms. For a training set
1692.2 Text Categorization Using Weight Adjusted k-Nearest Neighbor.. - Han, Karypis, Kumar (1999)(Correct)
Text categorization is the task of deciding whether a document belongs to a set of prespecified classes of documents.
Automatic classification schemes can greatly facilitate the process of categorizat... / algorithm like C . Qui or rule induction algorithms such as C . rules br W.W. Cohen. Fast effective rule induction. In Proc. of the Twelfth
1690.9 Data-Driven Theory Refinement Algorithms for Bioinformatics - Yang, Parekh, al.(Correct)
Bioinformatics and related applications call for
efficient algorithms for knowledge-intensive learning and
data-driven knowledge refinement. Knowledge based artificial
neural networks offer an attract... / ffl Approaches based on Rule Induction which use decision tree or br decision tree and rule induction techniques Bayesian networks
1679.3 Data Mining with Decision Trees and Decision Rules - Apte, Weiss (1997)(Correct)
This paper describes the use of decision tree and
rule induction in data mining applications. Of methods
for classification and regression that have been developed
in the fields of pattern recognition... / the use of decision tree and rule induction in data mining applications. br Keywords Decision Tree Rule Induction Data Mining Introduction
1676.0 MetaCost: A General Method for Making Classifiers Cost-Sensitive - Domingos (1999)(Correct)
Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, w... / to it now exist including rule induction decision tree br P. Domingos. Linear-time rule induction. Proc. nd Intl. Conf. on
1568.0 Learning a Local Similarity Metric for Case-Based Reasoning - Ricci, Avesani (1995)(Correct)
This paper presents a new class of local similarity metrics,
called AASM, that are not symmetric and that can be adopted as the
basic retrieval method in a CBR system. An anytime learning procedure
... / which is exploited for rule induction is also presented in br . . P. Domingos. Rule induction and instance-based learning a
1554.2 RIAC: A Rule Induction Algorithm Based on Approximate Classification - Howard Hamilton (1996)(Correct)
We present the RIAC (Rule Induction through Approximate Classification) method for inducing
rules from examples, based on the theory of rough sets. Imprecise data are generalized using a rough-sets
ba... / RIAC A Rule Induction Algorithm Based on br ISBN - - -X RIAC A Rule Induction Algorithm Based on
1551.4 Self-learning techniques for grapheme-to-phoneme conversion - Yvon (1994)(Correct)
In this article, we present a comprehensive review of various experiences with different self-learning techniques applied to the task of converting a graphemic string into the corresponding phonemic s... / Existing Techniques . Rule induction Rule-based formalism has br approach at least as far as rule induction from scratch is concerned.
1543.8 Knowledge Discovery with FlexiMine - Carmel Domshlak (1998)(Correct)
FlexiMine is a KDD system designed as a testbed for ongoing data-mining research,
as well as a generic knowledge discovery tool for varied database domains.
Among its tasks are learning dependencies f... / the straightforward association rule induction process leads to no useful br meaningful summarization or rule induction. Without a taxonomy to create
1533.6 Boosting Applied to Word Sense Disambiguation - Escudero, Marquez, Rigau (2000)(Correct)
In this paper Schapire and Singer's AdaBoost.MH boosting
algorithm is applied to the Word Sense Disambiguation (WSD) problem.
Initial experiments on a set of 15 selected polysemous words show that... / including Decision Trees and Rule Induction algorithms. Unfortunately
1526.7 A Behavioral Notion of Subtyping - Liskov, Wing (1994)(Correct)
The use of hierarchy is an important component of object-oriented design. Hierarchy allows the use of type families, in which higher level supertypes capture the behavior that all of their subtypes ha... / to the lack of a data type induction rule. A practical consequence of
1511.2 Learning to Improve both Efficiency and Quality of Planning - Estlin, Mooney (1997)(Correct)
Most research in learning for planning has concentrated on efficiency gains. Another important goal is improving the quality of final plans. Learning to improve plan quality has been examined by a few... / control rules. . Control Rule Induction The goal of the induction br Example Analysis Control Induction Rule Figure Scope's
1507.6 Hybrid Learning of Search Control for Partial-Order Planning - Estlin, Mooney (1996)(Correct)
This paper presents results on applying a version of
the Dolphin search-control learning system to speed up a partial-order
planner. Dolphin integrates explanation-based and inductive learning
techn... / traces. Dolphin's control rule induction algorithm was also extended br example analysis control rule induction and program specialization.
1504.2 An Inductive Learning Algorithm for Production Rule Discovery - Mehmet Tolun(Correct)
Data mining is the search for relationships and global patterns that exist in large databases.
One of the main problems for data mining is that the number of possible relationships is very
large, thu... / data. Both AQ and CN are rule induction systems that are regarded as br and Aksoy which is a rule induction algorithm with an ability to
1493.8 Proof Theory for µCRL - Groote, Ponse (1991)(Correct)
A proof theory for the specification language CRL (micro CRL) is proposed. CRL consists of
process algebra extended with abstract data types. The proof theory is meant to formalize the
interaction b... / module about data contains an induction rule for many-sorted abstract data br Therefore we introduce an induction rule. In example . . based on
1480.1 Using Latent Semantic Indexing for Data Mining - Jiang (1997)(Correct)
Data Mining is the application of algorithms for extracting valuable information
from large databases in order to make important business decisions. This
study explores a new technique for data mining... / . . Decision Trees and Rule Induction . br include decision trees and rule induction Qui nonlinear regression
1475.0 Rule Induction With Probabilistic Rough Classifiers - Piasta, Lenarcik (1996)(Correct)
We present an algorithm ProbRough for inducing decision rules from data.
The algorithm combines all the positive aspects of rule induction systems
with the flexibility of the probabilistic representat... / Rule Induction With Probabilistic Rough br all the positive aspects of rule induction systems with the flexibility
1468.9 Improving POS Tagging Using Machine--Learning Techniques - Llu'is Arquez Horacio(Correct)
In this paper we show how machine learning
techniques for constructing and combining several
classifiers can be applied to improve the
accuracy of an existing English POS tagger
(M`arquez and Rodr'igu... / trees neural networks rule-induction systems etc.Several
1462.8 Toward an Exemplar-Based Computational Model for Cognitive Grammar - Daelemans (1998)(Correct)
An exemplar-based computational framework is presented which is compatible with
Cognitive Grammar. In an exemplar-based approach, language acquisition is modeled
as the incremental, data-oriented stor... / the data the decision tree and rule induction programme C . or Backprop br systems hand made or using rule-induction and statistical systems
1457.0 Using a Generalisation Critic to Find Bisimulations for Coinductive.. - Louise Dennis Alan (1996)(Correct)
Coinduction is a method of growing importance in reasoning about functional languages,
due to the increasing prominence of lazy data structures. Through the use of bisimulations
and proofs that obse... / be used to derive a form of the induction rule a lfp F mono F br the two rules and The induction rule is used to show that all
1454.5 Two-Way Induction - Domingos (1995)(Correct)
General-to-specific learners like ID3 and CN2 perform
well when the target concept descriptions are general,
but often have difficulties when they are specific
or mixed. This problem can be alleviated... / to that of general-tospecific rule induction algorithms . br P. Clark and R. Boswell. Rule induction with CN Some recent
1452.2 Making a Productive Use of Failure to Generate Witnesses for.. - Dennis, Bundy, Green (1999)(Correct)
this paper to indicate universally quantified variables.
6 Witnesses for Coinduction
( unknown Annals of Mathematics and Artificial Intelligence 0 (1999) ?--? 1
Making a Productive Use of Failure to... / is associated with the induction rule the greatest fixedpoint with br A general form of the induction rule is F A A
1449.6 Instance-Family Abstraction in Memory-Based Language Learning - van den Bosch(Correct)
ion in Memory-Based Language Learning
Antal van den Bosch
ILK / Computational Linguistics
Tilburg University
The Netherlands
Antal.vdnBosch@kub.nl
Abstract
Memory-based learning appears relatively
s... / with wildcards as in rise Rule Induction from a Set of Exemplars br between and of wrapped rule induction cycles needed to arrive at
1443.8 R-MINI: An Iterative Approach for Generating Minimal Rules from.. - Hong (1997)(Correct)
Generating classification rules or decision trees from examples has been a subject of intense study
in the pattern recognition community, the statistics community and the machine learning
community of... / paper describe another rule induction technique using only br Indurkhya Reduced Complexity Rule Induction Proceedings of the Twelfth
1431.6 Axiomatisations of Weak Equivalences for De Simone Languages - Ulidowski (1995)(Correct)
Aceto, Bloom and Vaandrager proposed in [ABV92] a procedure
for generating a complete axiomatisation of strong bisimulation
for process languages in the GSOS format. However, the choice operator
+, ... / possibly with one infinitary induction rule for any process language
1407.3 Data mining with sparse grids - Garcke, Griebel, Thess (2001)(Correct)
We present a new approach to the classification problem arising in data mining.
It is based on the regularization network approach but, in contrast to the other
methods which employ ansatz functions... / neighbor methods decision tree induction rule learning and memory-based
1402.9 Naive Bayes for Regression - Frank, Trigg, Holmes, Witten (1998)(Correct)
Despite its simplicity, the naive Bayes learning scheme performs well on most classification tasks, and is often significantly more accurate than more sophisticated methods. Although the probability e... / instance-based learning and rule induction on standard benchmark
1397.5 An Empirical Comparison of Discretization Methods - Ventura, Martinez (1995)(Correct)
This paper presents empirical
results obtained by using six different discretization methods as preprocessors to three different
supervised learners on several real-world problems. No discretization t... / discussion CN . CN is a rule induction algorithm that incorporates
1396.9 Rewriting Logic as a Metalogical Framework - Basin, Clavel, Meseguer (2000)(Correct)
A metalogical framework is a logic with an associated methodology that is used to
represent other logics and to reason about their metalogical properties. We propose
that logical frameworks can be g... / framework theory which has an induction rule for reasoning about such br C n g to derive an induction rule ind to prove properties
1396.6 Using Case-Based Reasoning to Acquire User Scheduling Preferences.. - Sycara, Zeng, Miyashita (1995)(Correct)
Production/Manufacturing scheduling typically involves the acquisition of user optimization preferences. The ill-structuredness of both the problem space and the desired objectives make practical sche... / knowledge acquisition. First rule induction require great computational br interactions. Third after rule induction contextual information
1385.4 Concept Features in Re:Agent, an Intelligent Email Agent - Boone (1998)(Correct)
An important issue in the application of machine learning techniques to information management tasks is the nature of features extracted from textual information. We have created an intelligent email ... / algorithms consisted of a rule-induction method and a k-nearest
1384.8 Three companions for first order data mining - De Raedt, Blockeel, Dehaspe, Van Laer (1998)(Correct)
Three companion systems, Claudien, ICL and Tilde, are
presented. They use a common representation for examples and hypotheses:
each example is represented by a relational database. This contrasts
wi... / induction e.g. C . and rule induction e.g. CN or AQ br Party. Running your favorite rule-induction algorithm could then result in
1379.9 A Simple Proof Checker For Real-Time Systems - Leung (1995)(Correct)
This thesis presents a practical approach to verifying real-time properties of VLSI designs.
A simple proof checker with built-in decision procedures for linear programming and
predicate calculus offe... / . . Induction Rule br . . Induction Rule
1374.3 Proving Properties of Security Protocols by Induction - Paulson (1997)(Correct)
Informal justifications of security protocols involve arguing backwards that
various events are impossible. Inductive definitions can make such arguments
rigorous. The resulting proofs are complicated... / can perform. The corresponding induction rule lets us reason about the
1347.4 Data Mining and Document Modeling - Honkela(Correct)
Introduction
The amount of electronically available data has grown rapidly because of
increase in use of electronic data gathering devices, e.g., point-of-sale, remote
sensing devices etc., and becau... / data mining including ffl rule induction ffl statistics ffl br in an object. ffl Rule induction. A data mine system has to
1347.0 Design and Evaluation of the RISE 1.0 Learning System - Domingos (1994)(Correct)
Current rule induction systems (e.g. CN2) typically rely on a "separate
and conquer" strategy: they induce one rule at a time, removing the newly
covered examples from the training set after each step... / Abstract Current rule induction systems e.g. CN typically br problem is that a typical rule induction system e.g. CN or FOIL
1340.1 Reducing Redundancy in Characteristic Rule Discovery by Using.. - Brijs, Vanhoof, Wets (2000)(Correct)
The discovery of characteristic rules is a well-known data mining technique and has lead to several
successful applications. Unfortunately, typically a (very) large number of rules is discovered duri... / the CHRIS Characteristic Rule Induction by Subspace search rule br Induction by Subspace search rule induction algorithm which uses a
1339.8 Rule Induction as Exploratory Data Analysis - Catlett(Correct)
This paper examines induction of decision rules for purposes of exploratory data analysis,
and presents various tools and techniques for this. Decision tables provide a compact, consistent
format that... / Rule Induction as Exploratory Data Analysis br that have to be made by any rule induction algorithm a variety of very
1338.0 Building Intelligent Learning Database Systems - Wu (2000)(Correct)
Induction and deduction are two opposite operations in data mining applications. Induction
extracts knowledge in the form of, say, rules or decision trees from existing data, and deduction
applies i... / in an ad hoc way to implement rule induction from or data mining in br an interactive manner and Rule Induction which constructs decision
1331.9 Learning Declarative Control Rules for Constraint-Based Planning - Huang, Selman, Kautz (2000)(Correct)
Despite the long history of research in using
machine learning to speed-up state-space
planning, the techniques that have been developed
are not yet in widespread use in practical
planning systems... / examples together with a rule induction algorithm can learn useful br of supervised learning and rule induction. The systems of Khardon
1300.2 Learning and Reasoning as Information Compression by Multiple.. - Wolff(Correct)
This article presents the tentative idea that `multiple alignment' in a sense which is close to the use
of that term in bio-informatics, together with the full or partial merging or `unification' unkn... / may be applied to tasks like rule induction for expert systems data
1270.9 Verifying Invariants Using Theorem Proving - Graf, Saidi (1996)(Correct)
Our goal is to use a theorem prover in order to verify invariance
properties of distributed systems in a "model checking like"
manner. A system S is described by a set of sequential components, each... / a deduction rule rewriting rule induction scheme or a decision
1268.1 Applications of Machine Learning and Rule Induction - Langley, Simon (1995)(Correct)
An important area of application for machine learning is in automating the acquisition of knowledge
bases required for expert systems. In this paper, we review the major paradigms for machine
learning... / of Machine Learning and Rule Induction Pat Langley Pi br methods genetic learning rule induction and analytic approaches. We
1256.2 Reflective Metalogical Frameworks - Basin, Clavel, Meseguer (1999)(Correct)
A metalogical framework is a logic with an associated methodology that is used to represent
other logics and to reason about their metalogical properties. We propose that logical
frameworks can be g... / speci cation. For example the rule induction below rewrites a sub goal br the module ITP with a new rule induction This rule generates the
1255.4 Induction of Selective Bayesian Classifiers - Langley, Sage (1994)(Correct)
In this paper, we examine previous work on the naive
Bayesian classifier and review its limitations, which include
a sensitivity to correlated features. We respond
to this problem by embedding the nai... / learned as well as both rule-induction and decision-tree methods on
1254.1 Experience with Learning Agents which Manage Internet-Based.. - Edwards (1996)(Correct)
To provide assistance with tasks such as retrieving USENET news articles or identifying interesting Web pages, an intelligent agent requires information about a user's interests and needs. Machine lea... / agents Sheth symbolic rule induction algorithms such as C . br over the use of a symbolic rule induction algorithm for learning
1240.2 An RBF Network Alternative for a Hybrid Architecture - Peterson, Sun (1998)(Correct)
Although our previous model CLARION has
shown some measure of success in reactive sequential decision
making tasks by utilizing a hybrid architecture which
uses both procedural and declarative learnin... / Learning with Adaptive Rule Induction ON-line is one model
1223.6 A Family of Efficient Rule Generators - Liu(Correct)
This paper describes a family of rule generators that can be used to extract
classification rules in various applications. It includes versions that can handle
noise in data, that can produce perfec... / its relevance in decision tree rule induction but is more related to br the training data for rule induction is noise free and all
1222.7 Reasoning with Actions - Lassen(Correct)
Action semantics is a semantic description framework with very good
pragmatic properties but a rather weak theory for reasoning about programs. A
strong action theory would be of great practical use, ... /
1216.7 Automated Learning of Decision Rules for Text Categorization - Apte, Damerau, Weiss (1994)(Correct)
We describe the results of extensive experiments on large document collections using optimized
rule-based induction methods. The goal of these methods is to automatically discover
classification pat... / Learning Text Categorization Rule Induction Introduction Assigning br the article it represents. For rule induction the objective is to find sets
1216.3 Rule Induction for Semantic Query Optimization - Chun-Nan Hsu (1994)(Correct)
Semantic query optimization can dramatically
speed up database query answering by
knowledge intensive reformulation. But the
problem of how to learn required semantic
rules has not previously been sol... / Rule Induction for Semantic Query
1209.6 Automatic Construction of Descriptive Rules - Ramos (1997)(Correct)
The automatic inductive learning of production rules in a classification environment is a difficult process which requires several considerations and techniques to be studied. This is more noticeable ... / . Rule Induction . The sort of rules . br selection learning selectors rule induction and rule compaction are
1206.2 Combining Divide-and-Conquer and Separate-and-Conquer for Efficient.. - Boström, Asker(Correct)
Divide-and-Conquer (DAC) and Separate-and-Conquer
(SAC) are two strategies for rule induction that have been used extensively.
When searching for rules DAC is maximally conservative w.r.t.
decisions... / for Efficient and Effective Rule Induction Henrik Bostrom and Lars br SAC are two strategies for rule induction that have been used
1204.6 A Genetic Programming Framework for Two Data Mining Tasks.. - Freitas(Correct)
This paper proposes a genetic
programming (GP) framework for two
major data mining tasks, namely
classification and generalized rule
induction. The framework emphasizes
the integration between a GP al... / Classification and Generalized Rule Induction. Alex A. Freitas br classification and generalized rule induction. The framework emphasizes
1202.9 A Fixedpoint Approach to (Co)Inductive and (Co)Datatype Definitions - Paulson (1998)(Correct)
This paper presents a fixedpoint approach to inductive definitions. Instead
of using a syntactic test such as "strictly positive," the approach lets
definitions involve any operators that have been pr... / definitions it is strong rule induction for datatype definitions br rules . The basic induction rule . .
1196.8 A Bottom-Up Model of Skill Learning - Sun, Merrill, Peterson (1998)(Correct)
We present a skill learning model CLARION. Different from
existing models of high-level skill learning that use a topdown
approach (that is, turning declarative knowledge into
procedural knowledge), w... / Learning with Adaptive Rule Induction ONline. It embodies the
1190.5 Data Mining with Extended Symbolic Models - Apte, Pednault, Weiss (1998)(Correct)
Symbolic modeling of data with decision trees or decision
rules has a certain appeal to data mining application
developers. The computationally efficient
nature of the modeling methodology, and the... / decision tree rule induction neural networks etc. br of predictive modeling based on rule induction. Insurance companies collect
1187.2 A New MDL Measure for Robust Rule Induction - Pfahringer (1995)(Correct)
We present a generalization of a particular Minimum Description Length
(MDL) measure that sofar has been used for pruning decision trees only.
The generalized measure is applicable to (propositional) ... / A New MDL Measure for Robust Rule Induction Bernhard Pfahringer br both a stopping criterion for rule induction and as a criterion to choose
1182.0 Bayesian Networks, Rule Induction and Logistic Regression in the.. - Larrañaga, Gallego, Sierra.. (2000)(Correct)
In this paper we present an empirical comparison among several
paradigms coming from Statistics and Arti cial Intelligence for solving
a supervised classi cation problem. The empirically compared
para... / Bayesian Networks Rule Induction and Logistic Regression in br are Bayesian Networks Rule Induction and Logistic Regression. The
1170.9 A New Supervised Learning Algorithm for Word Sense Disambiguation - Pedersen(Correct)
The Naive Mix is a new supervised learning algorithm that is based on a sequential method for selecting probabilistic models. The usual objective of model selection is to find a single model that adeq... / such as decision trees C . rule induction CN and nearest-neighbor br CN Clark Niblett A rule induction algorithm that selects rules
1157.2 The CN2 Induction Algorithm - Clark, Niblett (1989)(Correct)
Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evalua... / Keywords concept learning rule induction noise comprehensibility
1146.4 Selective Sampling for Nearest Neighbor Classifiers - Lindenbaum, al. (1999)(Correct)
Most existing inductive learning algorithms assume the availability of a training set of labeled
examples. In many domains, however, labeling the examples is a costly process that requires
either i... / for the C rule-induction algorithm Lewis Catlett
1139.8 Deriving Denotational Semantics from Axiomatic Semantics within.. - Lewington, Henson(Correct)
In this paper we provide a constructive interpretation of Hoare logics with the constructive and
intensional theory of program development TK. As a result it becomes possible to derive
denotational se... / rule given above is the induction rule associated with the inductive br our induction schema that the induction rule associated with this
1137.3 Representing Arguments as Background Knowledge for Constraining.. - Peter Clark (1988)(Correct)
The use of statistical measures to constrain generalisation
in learning systems has proved successful in many
domains, but can only be applied where large numbers of
examples exist. In domains where f... / number of examples is met rule induction methodology has proved br This contrasts with the rule induction' methodology of delineating
1132.3 Forming Concepts for Fast Inference - Kautz, Selman (1992)(Correct)
Knowledge compilation speeds inference by creating tractable approximations of a knowledge
base, but this advantage is lost if the approximations are too large. We show how learning
concept generaliza... / theory. We also give a general induction rule for generating such concept br fit the pattern of the concept induction rule so we introduce a symbol
1132.1 RAMP: Rules Abstraction for Modeling and Prediction - Apte, Hong, Lepre, Prasad, Rosen (1995)(Correct)
ion for Modeling and
Prediction
C. Apte, S.J. Hong, J. Lepre, S. Prasad, and B. Rosen
IBM Research Division Technical Report
RC-20271
RAMP: Rules Abstraction for Modeling and Prediction
Chidanan... / classifiers on a large scale. Rule induction i.e.generating decision br Classification Modeling with Rule Induction Modern classification
1129.9 Lazy Induction Triggered by CBR - Mario Lenz(Correct)
In recent years, case-based reasoning has been demonstrated
to be highly useful for problem solving in complex domains.
Also, mixed paradigm approaches emerged for combining CBR and induction
techni... / Case-based reasoning rule induction lazy induction. br e.g. in Rule induction vs. prototype learning
1129.2 Inductive Lexica - Daelemans, Durieux (2000)(Correct)
Machine Learning techniques are useful tools for the automatic extension
of existing lexical databases. In this paper, we review some symbolic machine
learning methods which can be used to add new l... / conceptual clustering and rule induction approaches are eager br of earlier experiences as in rule induction and rule-based processing.
1125.3 Automated Text Categorization Using Support Vector Machine - Kwok (1998)(Correct)
In this paper, we study the use of support vector machine
in text categorization. Unlike other machine learning techniques,
it allows easy incorporation of new documents into
an existing trained syste... / system Recently automatic rule induction techniques have also been
1111.3 Database Mining through Inductive Logic Programming - Himanshu Gupta(Correct)
Rapid growth in the automation of business
transactions has lead to an explosion in the size of
databases. It has been realised for a long time that the
data in these databases contains hidden informa... / of the approaches is based on rule induction based techniques. This br suitable for clustering and rule induction techniques are suitable for
1098.9 Lookahead and Pathology in Decision Tree Induction - Murthy, Salzberg (1995)(Correct)
The standard approach to decision tree induction
is a top-down, greedy algorithm that
makes locally optimal, irrevocable decisions at
each node of a tree. In this paper, we empirically
study an altern... / the context of decision tree or rule induction. With the rapid increases in
1094.5 Exploiting Learning Technologies for World Wide Web Agents - Edwards, Green, Lockier, Lukins (1997)(Correct)
This paper illustrates how machine learning techniques can be utilised within intelligent software agents which assist users with the management of Web-based information. We discuss a number of recent... / including the C . rule induction algorithm and the IBPL br instance-based algorithm. Rule induction algorithms take a collection
1072.2 Machine Learning - Mooney(Correct)
This chapter introduces symbolic machine learning in which decision trees, rules, or casebased
classi ers are induced from supervised training examples. It describes the representation
of knowledge ... / induction of decision trees rule induction including inductive logic br Quinlan . Rule Induction Classi cation functions can
1061.7 Rule discovery from time series - Das, Lin, Mannila, Renganathan, Smyth (1998)(Correct)
We consider the problem of finding rules relating patterns
in a time series to other patterns in that series,
or patterns in one series to patterns in another series.
A simple example is a rule suc... / as the basis for exploratory rule induction. A time series can be br of VQ combined with rule induction to signal understanding
1047.7 Metadata-Supported Automated Ecological Modelling - Brilhante, Robertson(Correct)
Introduction
Ecological models should be rooted in data derived from observation, allowing methodical model construction and clear accounts
of model results with respect to the data. Unfortunately, m... / the series presents the use of rule induction algorithms to classify water br models. II. Data analysis with rule induction. Ecological Modelling
1045.0 Classification as Mining and Use of Labeled Itemsets - Meretakis, Wüthrich (1999)(Correct)
We investigate the relationship between association and
classification mining. The main issue in association mining is the
discovery of interesting patterns of the data, so called itemsets.
We introdu... / are decision trees and rule induction probabilistic br Decision tree and rule induction methods aim at finding a
1042.2 Induction in Noisy Domains - Clark, Niblett (1987)(Correct)
This paper examines the induction of classification rules from examples
using real-world data. Real-world data is almost always characterized by two
features, which are important for the design of an ... / . Introduction Automatic rule induction systems for inducing br that a relatively simple rule induction algorithm is able to achieve
1035.3 On the Process of Making Descriptive Rules - Riaño(Correct)
The automatic inductive learning of production rules in a classification environment is
a difficult process which requires several considerations and techniques to be studied. This
is more noticeable ... / key words symbolic learning rule induction. Introduction Within br learning selectors rule induction and rule compaction are
1033.7 Bayesian Model Averaging in Rule Induction - Domingos (1997)(Correct)
Bayesian model averaging (BMA) can be seen as the optimal approach to any
induction task. It can reduce error by accounting for model uncertainty in a principled
way, and its usefulness in several are... / Bayesian Model Averaging in Rule Induction Pedro Domingos br few attempts to apply it to rule induction have been made. This paper
1033.0 On Rough Sets and Inference Analysis - Zhang(Correct)
In this paper, we give an overview of a promising approach to
inference detection and analysis in relational databases, first introduced
in [25]. The approach employs techniques from rough sets theo... / inference threats posed by rule-induction techniques from data mining. br primary approaches in KDD is rule induction or learning from examples
1030.0 An Evaluation of Statistical Approaches to Text Categorization - Yang (1997)(Correct)
This paper is a comparative study of text categorization methods. Fourteen methods are investigated, based on
previously published results and newly obtained results from additional experiments. Corpu... / that make optimized rule induction particularly suitable.This br requires future research. The rule induction algorithms SWAP- RIPPER and
1029.7 Generating C4.5 Production Rules In Parallel - Kufrin (1997)(Correct)
Induction systems that represent concepts in the form
of production rules have proven to be useful in a variety
of domains where both accuracy and comprehensibility
of the resulting models are importa... / improving the performance of rule induction systems by exploiting br Smyth Rule induction systems are particularly
1029.0 Sparse Representations for Fast, One-Shot Learning - Yip, Sussman (1998)(Correct)
Humans rapidly and reliably learn many kinds of regularities and generalizations.
We propose a novel model of fast learning that exploits the properties
of sparse representations and the constraints i... / representation fast learning rule induction language learning Contact
1023.6 Data Mining: A Tightly-Coupled Implementation on a Parallel Database.. - Sousa, Mattoso, Ebecken(Correct)
Recent years have shown the need of an automated process to discover interesting and
hidden patterns in real-world databases, due to the difficulty of analyzing large volumes
of data using only OLAP t... / a series of primitives for rule induction RI after analyzing many br . . Typical Queries in Rule Induction Algorithms Most of queries
1016.1 Speeding-up Logic Programs by Combining EBG and FOIL - John Zelle (1992)(Correct)
This paper presents an algorithm that combines
traditional EBL techniques and recent
developments in inductive logic programming
to learn effective clause selection rules for
Prolog programs. When the... / example analysis control rule induction and program specialization. br decision examples to do control rule induction. Table Examples of useful
1015.3 Using Hybrid Connectionist Learning for Improving Speech/Language.. - Weber, Wermter(Correct)
In this paper we describe a screening approach for speech/
language analysis using learned, flat connectionist representations. For
investigating this approach we built a hybrid connectionist system... / Towell and Shavlik dealt with rule induction and Wermter br M. C. Mozer P. Smolensky. Rule induction through integrated symbolic
1014.6 An Integration of Deductive Retrieval into Deductive Synthesis - Fischer, Whittle (1999)(Correct)
Deductive retrieval and deductive synthesis are two conceptually
closely related software development methods
which apply theorem proving techniques to support the construction
of correct programs. In... / equalities and a well-founded induction rule to introduce recursion. The br context. Finally the induction rule must be used in a bottom-up
1012.7 Jan Paredis, MATRIKS, Universiteit Maastricht - Po Box Nl- (2000)(Correct)
Lists of if-then rules (i.e. ordered rule sets)
are among the most expressive and intelligible
representations for inductive learning
algorithms. Two extreme strategies searching
for such list of ... / Rule Induction with a Genetic Sequential br with some well known other rule induction systems each of them
1012.5 Skill Learning Using A Bottom-Up Hybrid Model - Sun, Merrill, Peterson (1998)(Correct)
This paper presents a skill learning model
Clarion. Different from existing models
of mostly high-level skill learning that use a
top-down approach (that is, turning declarative
knowledge into proced... / Learning with Adaptive Rule Induction ON-line Sun et al It
1008.2 Machine Learning of Phonotactics: Bibliography - Sang (1998)(Correct)
van den Bosch (eds.), Proceedings of the 7th Belgian-Dutch
Conference on Machine Learning, BENELEARN-97. 1997.
Bouma, H.H.W. (1997). Learning Dutch Phonotactics with Neural Networks. Master
thesis, ... / Learning Bias and Phonological Rule Induction'In Computational
1004.5 Black Box Views of State Machines - Breitling, Philipps (1999)(Correct)
System specification by state machines together with property specification and
verification by temporal logics are by now standard techniques to reason about
the control flow of hardware components, ... / . . . Induction Rule . br of . . Induction Rule Non-trivial progress proofs
1000.7 A case study in machine-assisted proofs: The Integers form an.. - Betarte (1993)(Correct)
We present a formalization of the set Z of integers using Martin-Lof's type theory.
In particular we focus on the task of proving that this set with the operations + and
form an Integral Domain. The... / as a kind of structural induction rule. The introduction and br in a similar way. The derived induction rule associated to the induction