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.
183 Fast Effective Rule Induction - Cohen (1995)(Correct)
Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collectio... / ML Fast Effective Rule Induction William W. Cohen AT T br IREP to more mature tree and rule induction methods. In the course of
178 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
104 The Inductive Approach to Verifying Cryptographic Protocols - Paulson (1998)(Correct)
Informal arguments that cryptographic protocols are secure can be made rigorous using inductive definitions. The approach is based on ordinary predicate calculus and copes with infinite-state systems.... / can perform. The corresponding induction rule lets us reason about the
104 Rippling: A Heuristic for Guiding Inductive Proofs - Bundy, Stevens, van Harmelen.. (1993)(Correct)
We describe rippling: a tactic for the heuristic control of the key part of proofs by mathematical induction. This tactic significantly reduces the search for a proof of a wide variety of inductive th... / an appropriate induction rule and induction variable. We showed how we br The first is the simplest induction rule Peano induction for natural
96 Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents - Tan (1993)(Correct)
Intelligent human agents exist in a cooperative
social environment that facilitates
learning. They learn not only by trialand
-error, but also through cooperation by
sharing instantaneous information,... / problem-solving approach to rule induction by dividing data among br problem-solving approach to rule induction learning in distributed
80 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
78 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
74 An Analysis of Bayesian Classifiers - Langley, Iba, Thompson (1992)(Correct)
In this paper we present an average-case analysis of the Bayesian classifier, a
simple probabilistic induction algorithm that fares remarkably well on many
learning tasks. Our analysis assumes a monot... / for decision-tree and rule induction they found that it performed
70 Beyond Independence: Conditions for the Optimality of the Simple.. - Domingos, Pazzani (1996)(Correct)
The simple Bayesian classifier (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits... / Cost Salzberg and rule induction CN Clark Boswell br Clark P. Boswell R. Rule induction with CN Some recent
60 Rule Induction with CN2: Some Recent Improvements - Clark, Boswell (1991)(Correct)
The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we ... / Rule Induction with CN Some Recent br made. Keywords learning rule induction CN Laplace noise
56 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
55 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
46 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
45 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
42 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
39 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
38 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
37 A Neural Network Approach to Topic Spotting - Wiener, Pedersen, Weigend (1995)(Correct)
This paper presents an application of
nonlinear neural networks to topic spotting.
Neural networks allow us to model higherorder
interaction between document terms
and to simultaneously predict multip... / Damerau and Weiss used a rule induction technique called Swap- to
37 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
36 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... /
35 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
35 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
28 Combining Classifiers in Text Categorization - Larkey (1996)(Correct)
Three different types of classifiers were investigated in the
context of a text categorization problem in the medical domain:
the automatic assignment of ICD9 codes to dictated
inpatient discharge sum... / relevance feedback and rule-induction algorithms from machine
27 Comparative Experiments on Disambiguating Word Senses: An.. - Mooney (1996)(Correct)
This paper describes an experimental comparison
of seven different learning algorithms on the
problem of learning to disambiguate the meaning
of a word from context. The algorithms tested
include stat... / bias for simple decision trees rule induction methods have a bias for br performance difference between rule induction and neural-networks on this
26 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
25 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
25 Towards Language Independent Automated Learning of Text.. - Apte, Damerau, Weiss (1994)(Correct)
We describe the results of extensivemachine learning experiments on large collections of
Reuters' English and German newswires. The goal of these experiments was to automatically
discover classifica... / article it represents. For rule induction the objective is to find sets br approach is that we will use a rule induction model for our representation.
22 A Common LISP Hypermedia Server - Mallery (1994)(Correct)
A World-Wide Web (WWW) server was implemented in Common LISP in order to
facilitate exploratory programming in the global hypermedia domain and to provide access to complex
research programs, partic... / applications such as automatic rule induction and natural- language br of the development period Rule Induction Learn if-then rules over
22 A Proof of the Kahn Principle for Input/Output Automata - Nancy Lynch (1989)(Correct)
We use input/output automata to define a simple and general model of networks of concurrently
executing, nondeterministic processes that communicate through unidirectional, named
ports. A notion of t... / it permits the use of Scott's induction rule to prove properties of process
20 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.
20 Rule Induction through Integrated Symbolic and Subsymbolic Processing - Clayton Mcmillan (1992)(Correct)
We describe a neural network, called RuleNet, that learns explicit, symbolic
condition-action rules in a formal string manipulation domain.
RuleNet discovers functional categories over elements of the... / Rule Induction through Integrated Symbolic br of a general methodology for rule induction in neural networks. This
18 Combining FOIL and EBG to Speed-up Logic Programs - John Zelle (1993)(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 br examples to do control rule induction. naivesort A B C D E
18 Theory and Practice of Action Semantics - Mosses (1996)(Correct)
Action Semantics is a framework for the formal description
of programming languages. Its main advantage over other frameworks
is pragmatic: action-semantic descriptions (ASDs) scale up smoothly to
... /
17 Static Versus Dynamic Sampling for Data Mining - John (1996)(Correct)
As data warehouses grow to the point where one
hundred gigabytes is considered small, the computational
efficiency of data-mining algorithms on large
databases becomes increasingly important. Using a
... / methods for decisiontree and rule induction. The algorithm runs in time
17 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
16 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
16 Detecting Intrusions Using System Calls: Alternative Data Models - Christina Warrender (1999)(Correct)
Intrusion detection systems rely on a wide variety of observable
data to distinguish between legitimate and illegitimate
activities. In this paper we study one such observable---
sequences of system c... / of different sequences a rule induction technique and Hidden Markov br W. W. Cohen. Fast effective rule induction. In Machine Learning the
16 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
16 Using Qualitative Models to Guide Inductive Learning - Clark, Matwin (1993)(Correct)
This paper presents a method for using qualitative
models to guide inductive learning.
Our objectives are to induce rules which
are not only accurate but also explainable
with respect to the quali... / and the learning technique is rule induction from data. Our method is br of commercially applying rule induction estimates that in typical
16 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
15 Multistrategy Learning for Information Extraction - Freitag (1998)(Correct)
Information extraction (IE) is the problem
of filling out pre-defined structured summaries
from text documents. We are interested
in performing IE in non-traditional
domains, where much of the text is... / classification and relational rule induction. By building regression
14 Genetic Algorithms as a Tool for Feature Selection in Machine Learning - Vafaie, De Jong (1992)(Correct)
This paper describes an approach being explored to improve
the usefulness of machine learning techniques for
generating classification rules for complex, real world data.
The approach involves the use... / a front end to traditional rule induction systems in order to identify br of features to be used by the rule induction system. This approach has been
13 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
13 Robust Decision Trees: Removing Outliers from Databases - John (1995)(Correct)
Finding and removing outliers is an important problem
in data mining. Errors in large databases can
be extremely common, so an important property of a
data mining algorithm is robustness with respe... / the C . decision tree and rule induction algorithm explaining the
12 Searching for Structure in Multiple Streams of Data - Oates (1996)(Correct)
Finding structure in multiple streams of data is an important problem. Consider the streams of data flowing from a robot's sensors, the monitors in an intensive care unit, or periodic measurements of ... / space. Our approach to rule induction from databases differs from br right-handsides. Most existing rule induction methods return rules that use
12 The Use of Proof Plans to Sum Series - Toby Walsh (1992)(Correct)
We describe a program for finding closed form solutions to finite sums. The
program was built to test the applicability of the proof planning search control
technique in a domain of mathematics outwit... / are provided by the form of induction rule used. For instance in the br these are provided by the induction rule in a natural way. In this
12 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
12 Post-Analysis of Learned Rules - Liu, Hsu(Correct)
Rule induction research implicitly assumes that after producing the rules from a dataset, these rules will be used directly by an expert system or a human user. In real-life applications, the situatio... / whsu iscs.nus.sg Abstract Rule induction research implicitly assumes br This is because classification rule induction is perhaps the most
11 A Package for Inductive Relation Definitions in HOL - Melham (1992)(Correct)
This paper describes a set of theorem proving tools
based on a new derived principle of definition in HOL,
namely the introduction of relations inductively defined
by a set of rules. Such inductive de... / under all the rules. . Rule induction By virtue of its definition br principle. This principle of rule induction is essential for many proofs
11 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
10 Linear-Time Rule Induction - Domingos(Correct)
The recent emergence of data mining as a
major application of machine learning has led
to increased interest in fast rule induction algorithms.
These are able to efficiently process
large numbers of e... / Linear-Time Rule Induction Pedro Domingos br to increased interest in fast rule induction algorithms. These are able to
10 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
10 Generating Accurate Rule Sets Without Global Optimization - Frank, Witten (1998)(Correct)
The two dominant schemes for rule-learning, C4.5 and RIPPER, both operate in two stages. First they induce an initial rule set and then they refine it using a rather complex optimization stage that di... / This paper presents a rule-induction procedure that avoids global br stopping criterion. It follows rule induction with a post-processing step
10 Verification of Compiler Correctness for the WAM - Pusch (1996)(Correct)
Relying on a derivation of the Warren Abstract Machine
(WAM) by stepwise refinement of Prolog models by Borger and Rosenzweig
we present a formalization of an operational semantics for Prolog.
Th... / theorem can be proved by rule induction i implies i config ok br distinctness and an induction rule. The types construct is used
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
8 Automatic Rule Induction for Unknown Word Guessing - Andrei Mikheev (1997)(Correct)
this
paper we present a technique for fully automatic acquisition of rules which guess possible
part-of-speech tags for unknown words using their starting and ending segments. The
learning is performe... / Automatic Rule Induction for Unknown Word Guessing br guessing rules first. The rule induction process is guided by a
8 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
8 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
8 Phonological Parsing for Bi-directional.. - Meng (1995)(Correct)
This thesis proposes a unified framework for integrating a variety of linguistic knowledge
sources for representing speech, in order to facilitate their concurrent utilization
in spoken language syste... /
7 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
7 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
7 Engineering Multiversion Neural-Net Systems - Partridge And(Correct)
In this paper we address the problem of constructing reliable neural-net implementations,
given the assumption that any particular implementation will not be
totally correct. The approach taken in thi... / who explored a variety of rule-induction approaches and demonstrated
7 Lean Induction Principles for Tableaux - Baaz, Egly, Fermüller (1997)(Correct)
In this paper, we deal with various induction principles incorporated
in an underlying tableau calculus with equality. The induction
formulae are restricted to literals. Induction is formalized as m... / There we shall use an induction rule that corresponds to the br ffi C c c triggers induction rule hhhhhhhhhh
7 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
7 Feature Selection Methods: Genetic Algorithms vs. Greedy-like Search - Haleh Vafaie (1994)(Correct)
This paper presents a comparison between two feature selection
methods, the Importance Score (IS) which is based on a greedy-like search and a
genetic algorithm-based (GA) method, in order to better... / The AQ algorithm is a rule induction technique used to produce a br The second step is to apply a rule induction process AQ in our
7 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
7 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
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
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
6 On the Use of the Constructive Omega-Rule within Automated Deduction - Baker Ireland (1992)(Correct)
The cut elimination theorem for predicate calculus states that every
proof may be replaced by one which does not involve use of the cut rule.
This theorem no longer holds when the system is extended w... / essentially it is the induction rule which is causing the problem br wish to propose some sort of induction rule to capture the idea not
6 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
6 Using Real-Valued Genetic Algorithms to Evolve Rule Sets for.. - Corcoran, Sen (1994)(Correct)
In this paper, we use a genetic algorithm
to evolve a set of classification rules with
real-valued attributes. We show how real-valued
attribute ranges can be encoded with real-valued
genes and presen... / future instances. An accurate rule induction mechanism improves quality of
6 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
6 The Induction of Rules for Predicting Chemical Carcinogenesis in.. - Bahler (1993)(Correct)
This paper presents results from an ongoing
e#ort in applying a variety of induction-based
methods to the problem of predicting the biological
activity of noncongeneric (structurally
dissimilar) c... / of experiments in tree and rule induction from a set of example br in supervised tree and rule induction from a training set of
5 Improving A Rule Induction System Using Genetic Algorithms - Haleh Vafaie (1994)(Correct)
The field of automatic image recognition presents a variety of difficult classification
problems involving the identification of important scene components in the presence of noise,
changing lighting ... / Improving A Rule Induction System Using Genetic br In this context standard rule induction systems like AQ produce sets
5 Multi-Layer Incremental Induction - Wu, Lo (1998)(Correct)
This paper describes a multi-layer incremental induction algorithm,
MLII, which is linked to an existing nonincremental induction
algorithm to learn incrementally from noisy data. MLII makes use of
... / Wu a nonincremental rule induction system that in many cases
5 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
5 Learning to Tag for Information Extraction from Text - Ciravegna (2000)(Correct)
LearningPINOCCHIO is an algorithm for adaptive information extraction. It learns template filling rules that insert SGML tags into texts. LearningPINOCCHIO is based on a covering algorithm that lear... / In this paper we report on rule induction for template filling only. br in producing templates. . Rule Induction LearningPINOCCHIO learns
4 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.
4 When Does Overfitting Decrease Prediction Accuracy in Induced.. - Cullen Schaffer (1991)(Correct)
Researchers studying classification techniques based on induced
decision trees and rule sets have found that the model which best fits
training data is unlikely to yield optimal performance on fresh d... / reports on decision tree and rule induction approaches to classification br what I believe is the smallest rule induction or decision tree problem for
4 Deconstructing the Digit Recognition Problem - Cullen Schaffer (1992)(Correct)
Decision tree pruning techniques and other
forms of overfitting avoidance have often
been considered statistical means of improving
predictive accuracy. Intuitively, they are
intended to determine the... / in neural networks rule induction and other data-driven br John . Expert systems rule induction with statistical data.
4 The RISE System: Conquering Without Separating - Pedro Domingos (1994)(Correct)
Current rule induction systems (e.g. CN2) typically
rely on a "separate and conquer" strategy, learning
each rule only from still-uncovered examples. This
results in a dwindling number of examples bei... / U.S.A. Abstract Current rule induction systems e.g. CN typically br in the Rise . system Rule Induction from a Set of Examples In
4 Relational Rippling: A General Approach - Bundy, Lombart (1995)(Correct)
We propose a new version of rippling, called
relational rippling. Rippling is a heuristic for
guiding proof search, especially in the step
cases of inductive proofs. Relational rippling
is designed f... / the induction conclusion by the induction rule. To initialise rippling the
4 Learning Classification Rules Using Lattices - Mehran Sahami (1995)(Correct)
This paper presents a novel induction algorithm, Rulearner, which induces
classification rules using a Galois lattice as an explicit map through the
search space of rules. The construction of lattices... / lattices decision lists rule induction Introduction Research br Introduction Research in rule induction by means of search Michalski
3 Fuzzy Interpretation of Induction Results - Xindong Wu (1995)(Correct)
When applying rules induced from training examples
to a test example, there are three possible
cases which demand different actions: (1) no
match, (2) single match, and (3) multiple match.
Existing t... / an ad hoc way to implement rule induction from databases or design some
3 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
3 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
3 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
3 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
3 Encoding Natural Semantics in Coq - Terrasse (1995)(Correct)
We address here the problem of automatically translating
the Natural Semantics of programming languages to Coq, in order to
prove formally general properties of languages. Natural Semantics [18] is
... / structural induction and rule induction often needed in the process br allowing structural induction and rule induction often needed in
3 1st Order Logic Formal Concept Analysis: from logic programming to.. - Chaudron, Maille (1998)(Correct)
In this paper, we analyze and define the introduction of 1st order
logic in Formal Concept Analysis (FCA); the aims are both
theoretical (as a complete model is needed) and applied (so as
to improve e... / Concepts Discovery Rule induction Logic Programming. Authors' br st order FCA the st order rule induction is not developed in the
3 A Bayesian Discretizer for Real-Valued Attributes - Xindong Wu (1996)(Correct)
Discretization of real-valued attributes into nominal intervals has
been an important area for symbolic induction systems because many
real world classification tasks involve both symbolic and numeric... / In the context of rule induction and decision tree
3 Neural Networks for Wordform Recognition - Eineborg, Gambäck (1994)(Correct)
The paper outlines a method for automatic lexical acquisition using three-layered
back-propagation networks. Several experiments have been carried out where the
performance of different network arc... / grammar and transfer-rule induction etc.have certainly not been
3 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
3 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
3 Formal Specification and Prototyping of a Program Specializer - Blazy, Facon(Correct)
This paper reports on the use of formal specifications in the development
of a software maintenance tool for specializing imperative programs, which have
become very complex due to extensive modific... / of these rules is proved using rule induction. A Prolog prototype has been br proof of correctness rule induction Centaur. Introduction
3 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.
3 Recent Advances in Memory-Based Part-of-Speech Tagging - Jakub Zavrel (1999)(Correct)
Memory-based learning algorithms are lazy learners. Examples of a task are stored
in memory and processing is largely postponed to the time when new instances of the
task need to be solved. This is th... / from earlier experiences as in rule induction and rule-based processing br experiences as in rule induction and rule-based processing An mbl
3 The Role of Domain Knowledge in Data Mining - Sarabjot Anand (1995)(Correct)
The ideal situation for a Data Mining or Knowledge Discovery
system would be for the user to be able to pose a query
of the form "Give me something interesting that could be
useful" and for the system... / within the STRIP Strong Rule Induction in Parallel algorithm br used by the STRIP Strong Rule Induction in Parallel algorithm for
3 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
3 An Investigation of Machine Learning Based Prediction Systems - Mair, Kadoda, Lefley, Phalp.. (1999)(Correct)
Traditionally, researchers have used either off-the-shelf models such as COCOMO, or
developed local models using statistical techniques such as stepwise regression, to obtain
software effort estimates... / case-based reasoning CBR and rule induction RI This paper outlines some br nets case-based reasoning rule induction software cost models
3 Discovering Comprehensible Classification Rules with a Genetic.. - Fidelis Lopes Freitas (2000)(Correct)
This work presents a classification algorithm
based on genetic algorithms (GAs) that discovers
comprehensible IF-THEN rules, in the spirit of data
mining. The proposed GA has a flexible chromosome
enc... / mining algorithms based on the rule induction paradigm. This paper is br used in data mining is still rule induction. Most of the algorithms in
3 Machine Learning in Automated Text Categorisation - Sebastiani (1999)(Correct)
this paper. Aside from (i) the automatic assignment
of documents to a predefined set of categories, which is the main topic of
this paper, the term has also been used to mean (ii) the automatic defini... / models decision tree and DNF rule induction methods profile-construction br than decision tree inducers. Rule induction methodsusually attempt to
3 A Simple Formalization and Proof for the Mutilated Chess Board - Lawrence Paulson Computer (1996)(Correct)
The impossibility of tiling the mutilated chess board has been formalized and verified using Isabelle. The formalization
is concise because it is expressed using inductive definitions. The proofs are ... / i n A primer on rule induction You are probably familiar br a principle sometimes known as rule induction. Given the definition of
3 Deductive Synthesis of Recursive Plans in Linear Logic - Cresswell, Smaill, Richardson (1999)(Correct)
Linear logic has previously been shown to be suitable for
describing and deductively solving planning problems involving conjunction
and disjunction. We introduce a recursively defined datatype an... / datatype and a corresponding induction rule thereby allowing recursive br logic with an appropriate induction rule for a recursive datatype
3 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
2 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
2 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
2 Proving a conjecture of Andreka on temporal logic - Schobbens Raskin (1998)(Correct)
In [3], a large number of completeness results about variants of discrete
linear-time temporal logic are obtained. One of them is left as an open problem:
the completeness of the logic of initially an... / Proof. We instantiate the induction rule fl fi by
2 A Formal Verification of the Alternating Bit Protocol in µCRL - Kamsteeg (1993)(Correct)
We present a formal verification proof for the Alternating Bit Protocol in the specification language and proof theory
of µCRL. A brief outline, dwelling on the more elaborate parts of the derivation,... / Therefore we find the induction rule -as it is -not very suitable br problem with the proviso on the induction rule is that it is not even
2 Unsupervised Discovery of Phonological Categories through Supervised.. - Walter Daelemans (1996)(Correct)
We describe a case study in the application of symbolic machine learning techniques for
the discovery of linguistic rules and categories. A supervised rule induction algorithm is
used to learn to pred... / and categories. A supervised rule induction algorithm is used to learn to br on our choice of C . as a rule induction mechanism. We chose it
2 Using Machine Learning to Enhance Software Tools for Internet.. - Claire Green (1996)(Correct)
This paper discusses the issues involved in the application
of machine learning techniques to the management
of Internet-based information. We present a general
architecture, and describe how this has... / to create such profiles rule induction and an instance-based method. br instance-based method. The CN rule induction algorithm Clark Niblett
2 A proof of Higman's lemma by structural induction - Coquand, Fridlender (1993)(Correct)
This paper gives an example of such an inductive proof for a combinatorial problem.
While there exist other constructive proofs of Higman's lemma (see for instance [10, 14]), the
present argument has ... / defined relations and proofs by rule inductions or inductions on the br so-called proof induction or rule induction that is by induction over
2 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
2 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
2 Foundations of Functional Programming - Paulson (1996)(Correct)
ions are compiled to the closure command, which will push a closure
onto the Stack. The closure will include the current Environment and will hold M
as a list of commands, from compilation:
[[x:M ]] ... / For this we need a new induction rule called structural induction. br x l Here is the list induction rule in symbols OE
2 Improving the Performance of a Rule Induction System Using Genetic.. - Vafaie, De Jong (1991)(Correct)
Concept acquisition is a form of inductive
learning that induces general descriptions of
concepts from specific instances of a given
concept. AQ15 is a conceptual inductive
learning program that uses ... / Improving the Performance of a Rule Induction System Using Genetic br problems namely the use of rule induction methodologies in particular
2 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
2 Formalization of the SPECTRUM Methodology in DEVA: Signature and.. - Santen (1993)(Correct)
The signature and logical calculus of the algebraic specification language Spectrum
are formalized in the generic language Deva. This language is designed to express formal
methods as well as proofs ... / Fixpoints and the Fixpoint Induction Rule br an axiom about fix f and the induction rule. Our formalization of
2 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 .
2 Discovering Interesting Prediction Rules with a Genetic Algorithm - Noda, Freitas, Lopes (1999)(Correct)
In essence, the goal of data mining is to
discover knowledge which is highly accurate,
comprehensible and "interesting" (surprising, novel).
Although the literature emphasizes predictive accuracy
and ... / facilitated by the use of rule induction algorithms including decision br mining methods are based on the rule induction paradigm where the algorithm
2 Experience with Rule Induction and k-Nearest Neighbour Methods for.. - Payne, Edwards, Green (1995)(Correct)
this paper use the same feature extraction mechanism, which extracts words according to word frequency. The underlying assumption here is that words which act as good classifiers for identifying messa... / Experience with Rule Induction and k-Nearest Neighbour br within this architecture a rule induction algorithm CN Clark and
2 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
2 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
2 Detecting image purpose in World-Wide Web documents - Paek, Smith (1998)(Correct)
The numberofWorld-Wide Web #WWW# documents available to users of the Internet is growing at an incredible
rate. Therefore, it is becoming increasingly important to develop systems that aid users in se... / tree learning for automated rule induction for the content image br system. Automatic rule-induction The heart of the system is a
2 Unique Fixpoint Induction for Message-Passing Process Calculi - Hennessy, Lin (1997)(Correct)
We present a proof system for message-passing process calculi with recursion.
The key inference rule to deal with recursive processes is a version of Unique
Fixpoint Induction for process abstractions... / definitions the Unique Fixpoint Induction rule as naively expressed above br using the Unique Fixpoint Induction rule above from the judgement x
2 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
2 Extensions to the Estimation Calculus - Gow, Bundy, Green (1999)(Correct)
Walther's estimation calculus was designed to prove the termination
of functional programs, and can also be used to solve the similar
problem of proving the well-foundedness of induction rules. Ho... / proving the well-foundedness of induction rules. However there are certain br more common to the problem of induction rule well-foundedness than the
2 Inverting Inductively Defined Relations in LEGO - McBride(Correct)
this paper. Its specification is clear, it is sound
and complete with respect to constructor forms and the tactic Qnify, to which
it gives rise, has proved independently useful. unknown Inverting Indu... / a related structure. Indeed rule induction for can be derived from its br perhaps one might have expected rule induction. We shall prove x
2 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
2 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
2 Generalisation for Induction - Vadera (1993)(Correct)
Proof by induction plays a central role in showing that recursive
programs satisfy their specification. Sometimes a key
step is to generalise a lemma so that its inductive proof is easier.
This rep... / is to select an appropriate induction rule. In this report we do not br suppose we use the following induction rule Jon N-ind p
2 Lightweight Rule Induction - Weiss, Indurkhya (2000)(Correct)
A lightweight rule induction method is described
that generates compact Disjunctive
Normal Form (DNF) rules. Each class has
an equal numberofunweighted rules. A new
example is classified by applyi... / Lightweight Rule Induction Sholom Weiss and Nitin br ICML Lightweight Rule Induction Sholom M. Weiss
2 An Extended Genetic Rule Induction Algorithm - Liu, Kwok (2000)(Correct)
This paper describes an extension of a GAbased,
separate-and-conquer propositional rule induction
algorithm called SIA [24]. While the original algorithm is
computationally attractive and is also able... / An Extended Genetic Rule Induction Algorithm Juliet Juan Liu br propositional rule induction algorithm called SIA
2 Using Decision Tree Induction for Discovering Holes in Data - Bing Liu Ke (1998)(Correct)
Existing research in machine learning and data mining has been focused
on finding rules or regularities among the data cases. Recently, it was
shown that those associations that are missing in dat... / S and S It used a rule induction system for the purpose. One of br Our work is different from rule induction e.g. and conceptual
2 Induction in Philosophy and AI - Bell(Correct)
Introduction
In this position paper I recall some of the philosphical background of
induction. I also discuss the relationship between induction and nonmonotonic
logics, and provide a general model-t... / by simple enumeration or rule induction. It also includes arguments
2 Learning Intonation Rules for Concept-to-Speech Generation - Pan (1998)(Correct)
We aim to design and develop a Concept-to-Speech (CTS) generation system, a speech synthesis
system producing speech from semantic representations, by integrating language generation with speech
synth... / tool. how to integrate rule induction into a CTS architecture to br a decision-tree based rule induction system to the simple model
2 Deductive Verification of Modular Systems - Finkbeiner, Manna, Sipma (1998)(Correct)
Effective verification methods, both deductive and algorithmic,
exist for the verification of global system properties. In this paper,
we introduce a formal framework for the modular description and... / with a compositional induction rule. . Example We br information. The induction rule makes the methodology
2 Error Driven Word Sense Disambiguation - Dini, Di Tomaso, Segond (1998)(Correct)
In this paper we describe a method for performing
word sense disambiguation (WSD). The
method relies on unsupervised learning and exploits
functional relations among words as produced
by a shallow par... / the experiment we ran using rule induction techniques on functional
2 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
2 Genetic Programming for Knowledge Discovery in Chest Pain Diagnosis - Bojarczuk, Lopes, Freitas (2000)(Correct)
This work aims at discovering classification rules for diagnosing certain pathologies. These rules
are capable of discriminating among 12 different pathologies, whose main symptom is chest
pain. In or... / mining algorithms including rule induction instance-based learning or br by C . a state-of-the-art rule induction algorithm The result of
2 Basic Action Theory - Lassen (1995)(Correct)
Action semantics is a semantic description framework with very good
pragmatic properties but until now a rather weak theory for reasoning
about programs. A strong action theory would have a great pr... /
2 Program Semantics and Classical Logic - Muskens (1997)(Correct)
In the tradition of Denotational Semantics one usually lets program
constructs take their denotations in reexive domains, i.e. in domains
where self-application is possible. For the bulk of programm... /
2 Unique Fixpoint Induction for Mobile Processes - Lin (1995)(Correct)
Complete proof systems for bisimulation equivalences in the -calculus
with recursion are presented. The key inference rule dealing with recursion
is unique
xpoint induction which generalises that ... /
2 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... /