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.
Constructing Conjunctive Attributes Using Production Rules - Zijian Zheng (2000)(Correct)
Many existing constructive decision tree learning algorithms
such as Fringe and Citre construct conjunctions or disjunctions directly
from paths of decision trees. This paper investigates a novel at... / Tree Learning Classification Machine Learning. CR Categories Computing br discovery in empirical learning. Machine Learning - .
Layered Learning - Stone, Veloso (2000)(Correct)
This paper presents "layered learning," a hierarchical
machine learning paradigm. Layered learning applies
to tasks for which learning a direct mapping from
inputs to outputs is in principle intractab... / learning a hierarchical machine learning paradigm. Layered learning
A Survey of Methods for Scaling Up Inductive Algorithms - Provost, Kolluri (1999)(Correct)
One of the defining challenges for the KDD research community is to enable inductive
learning algorithms to mine very large databases. By collecting, categorizing, and summarizing
existing work on s... / backgrounds including machine learning statistics and databases br discovery in empirical learning. Machine Learning - .
Markovian Models for Sequential Data - Bengio (1999)(Correct)
Hidden Markov Models (HMMs) are statistical models of sequential data that have been used
successfully in many machine learning applications, especially for speech recognition. We first
summarize the ... / used successfully in many machine learning applications especially for
Methods for Global Organization of the Protein Sequence Space - Yona (1999)(Correct)
r Bioccelerator and to their software.
It was a pleasure to work with Alex Kremer, Avi Kavas, Yoav Etsion and Daniel Avrahami,
the great team of students with whom I created the ProtoMap web site.
I t... / colleagues and friends in the machine learning lab with whom I spent most
Integrating case-based learning and cognitive biases for machine.. - Cardie (1999)(Correct)
This paper shows that psychological constraints on human information processing can be used effectively
to guide feature set selection for case-based learning of linguistic knowledge. Given as input a... / and cognitive biases for machine learning of natural language Claire br is harmful in language learning. Machine Learning - - .
Generalization and Generalizability Measures - Wah (1999)(Correct)
In this paper, we define the generalization problem, summarize various
approaches in generalization, identify the credit assignment problem,
and present the problem and some solutions in measuring gen... / lead to negative results. Machine learning in an area in artificial br in explanation based learning. Machine Learning pages -
A Brief Introduction to Boosting - Schapire (1999)(Correct)
Boosting is a general method for improving the
accuracy of any given learning algorithm. This
short paper introduces the boosting algorithm
AdaBoost, and explains the underlying theory
of boosting, in... / framework for studying machine learning called the PAC learning
Characterization and Parallelization of Decision Tree Induction - Bradford, Fortes (1999)(Correct)
This paper examines the performance and memory-access behavior of the C4.5 decision
tree induction program, a representative example of data mining applications,
for both uniprocessor and parallel imp... / one or more statistical machine learning or image processing br programming ILP and association rules and its extension
Encouraging cooperation in the genetic iterative rule learning.. - Cordon (1999)(Correct)
Genetic Algorithms have proven to be a powerful tool for automating the Fuzzy Rule Base definition and, therefore, they have been widely used to design descriptive Fuzzy Rule-Based Systems for Quali... / of interest in using GAs for machine learning problems Fuzzy Rule br Understanding the nature of learning Machine Learning An artificial
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique.. - Zheng, Webb, Ting (1999)(Correct)
Lbr is a lazy semi-naive Bayesian classifier
learning technique, designed to alleviate the
attribute interdependence problem of naive
Bayesian classification. To classify a test example,
it creates a ... / of domains from the UCI machine learning repository Blake Keogh br dis covery in empirical learning. Machine Learning .
Efficient exploration for optimizing immediate reward - Schuurmans, Greenwald (1999)(Correct)
We consider the problem of learning an effective behavior
strategy from reward. Although much studied, the
issue of how to use prior knowledge to scale optimal
behavior learning up to real-world probl... / are significant subareas of machine learning and neural network research. br C.and Dayan P. . Q-learning. Machine Learning Watkins
Programming by Demonstration: An Inductive Learning Formulation - Lau, Weld (1999)(Correct)
Although Programming by Demonstration (PBD) has
the potential to improve the productivity of unsophisticated
users, previous PBD systems have used brittle,
heuristic, domain-specific approaches to exe... / are based on well-understood machine learning technology. TGen vs uses br prior knowledge in inductive learning. Machine Learning - .
An Evaluation of Statistical Approaches to Text Categorization - Yang (1999)(Correct)
This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A... / Tree DTree is a well-known machine learning approach to automatic
Content-Based Book Recommending Using Learning for Text Categorization - Mooney, Roy (1999)(Correct)
Recommender systems improve access to relevant products
and information by making personalized suggestions based
on previous examples of a user's likes and dislikes. Most existing
recommender systems ... / information extraction and a machine-learning algorithm for text br generalization with active learning. Machine Learning -
Greedy Function Approximation: A Gradient Boosting Machine - Friedman (1999)(Correct)
Function approximation is viewed from the perspective of numerical optimization in
function space, rather than parameter space. A connection is made between stagewise additive
expansions and steepest-... / wavelet-like dictionary. In machine learning is called
On Bandwidth Smoothing - Maltzahn, Richardson, Grunwald.. (1999)(Correct)
The bandwidth usage due to HTTP traffic often varies
considerably over the course of a day, requiring high
network performance during peak periods while leaving
network resources unused during off-pea... / cache hit rates. We apply machine learning techniques to automatically
Statistical Models for Text Segmentation - Beeferman, BERGER, LAFFERTY (1999)(Correct)
This paper introduces a new statistical approach to automatically partitioning text
into coherent segments. The approach is based on a technique that incrementally builds an
exponential model to ext... / may be cast as a problem in machine learning learn how to place breaks
Implicit Imitation in Multiagent Reinforcement Learning - Price, Boutilier (1999)(Correct)
Imitation is actively being studied as an effective
means of learning in multi-agent environments.
It allows an agent to learn how to act
well (perhaps optimally) by passively observing
the actions of... / planning and teaching. Machine Learning . Michael br C. H. Watkins and P. Dayan. Q-learning. Machine Learning -
Comparing Bayesian Network Classifiers - Cheng, Greiner (1999)(Correct)
In this paper, we empirically evaluate
algorithms for learning four Bayesian network
(BN) classifiers: Naïve-Bayes, tree augmented
Naïve-Bayes (TANs), BN augmented NaïveBayes
(BANs) and general BNs (G... / deserve more attention in machine learning and data mining communities.
Advances in Large Margin Classifiers - Smola, Bartlett, Schölkopf, (Eds.) (1999)(Correct)
this paper are taken from (Herbrich et al., 1999)
Smola, Bartlett, Scholkopf, and Schuurmans: Advances in Large Margin Classifiers 1999/03/31 11:08 unknown Smola, Bartlett, Scholkopf, and Schuurmans... / -xxx-xxxxx-x alk. paper . Machine learning. . Algorithms. . Kernel br mapping the functions of the learning machine into some dot product space
Error-Correcting Output Coding for Text Classification - Berger (1999)(Correct)
This paper applies error-correcting output coding (ECOC) to the task of document categorization. ECOC, of recent vintage in the AI literature, is a method for decomposing a multiway classification pro... / much recent interest in the machine learning community about algorithms
Efficient Value Function Approximation Using Regression Trees - Wang, Dietterich (1999)(Correct)
Value function approximation is a problem central to reinforcement learning. Many applications of reinforcement learning have relied on neural network function approximators, which are very slow to tr... / International Conference on Machine Learning pp. - . Morgan br issues in temporal difference learning. Machine Learning - .
Distributed Value Functions - Schneider, Wong, Moore, Riedmiller (1999)(Correct)
Many interesting problems, such as power
grids, network switches, and traffic flow, that
are candidates for solving with reinforcement
learning (RL), also have properties that make
distributed solutio... / Function Approximation. In Machine Learning Proceedings of the Twelfth
Visually Aided Exploration of Interesting Association Rules - Liu, Hsu, Wang, Chen (1999)(Correct)
Association rules are a class of important regularities in databases. They
are found to be very useful in practical applications. However, the number of association
rules discovered in a database ca... / J. R. C . program for machine learning. Morgan Kaufmann . br Exploration of Interesting Association Rules Bing Liu Wynne Hsu Ke
Least-Squares Temporal Difference Learning - Justin Boyan (1999)(Correct)
Excerpted from: Boyan, Justin. Learning Evaluation Functions for Global Optimization. Ph.D.
thesis, Carnegie Mellon University, August 1998. (Available as Technical Report CMU-CS-98-152.)
TD() is a po... / temporal difference learning. Machine Learning - . br for temporal difference learning. Machine Learning
Software Sensor Design Based on Empirical Data - Masson, Canu, Grandvalet.. (1999)(Correct)
This paper
presents a methodology exploiting the redundancy arising in those databases to
replace missing measurements, or to cross-check available ones. This methodology
is illustrated on a case stud... / is truly non-linear. In the machine learning framework the goal of
Efficient Search of Reliable Exceptions - Liu, Lu, Feng, Hussain (1999)(Correct)
Finding patterns from data sets is a fundamental task of
data mining. If we categorize all patterns into strong, weak, and random,
conventional data mining techniques are designed only to find stro... / techniques from the fields of machine learning statistics and database br weak pattern mining. Taking association rule mining as an example all
Learning Quantitative Knowledge for Multiagent Coordination - David Jensen (1999)(Correct)
A central challenge of multiagent coordination is reasoning
about how the actions of one agent affect the
actions of another. Knowledge of these interrelationships
can help coordinate agents --- preve... / Learning and adaptation Machine Learning and Discovery Techniques or
Discovering Association Rules based on Image Content - Carlos Ordonez (1999)(Correct)
Our focus for data mining in this paper is concerned with
knowledge discovery in image databases. We present a data
mining algorithm to find association rules in 2-dimensional
color images. The algori... / intelligence expert systems machine learning and statistics. Many br Discovering Association Rules based on Image Content
Mining Optimized Support Rules for Numeric Attributes - Rastogi, Shim (1999)(Correct)
Mining association rules on large data sets has received considerable attention in recent years.
Association rules are useful for determining correlations between attributes of a relation and have
app... / Abstract Mining association rules on large data sets has
Scalability In Formal Concept Analysis - Cole, Eklund (1999)(Correct)
This paper presents the results of experiments carried out with a set of 4,000 medical discharge summaries in which were recognised 1,962 attributes from the Unified Medical Language System (UMLS). In... / FCA Wille is a machine learning formalism that allows
Active Learning for Natural Language Parsing and Information.. - Thompson, Califf, Mooney (1999)(Correct)
In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select f... / is an emerging area in machine learning that explores methods that br generalization with active learning. Machine Learning
A Hierarchical Approach to Wrapper Induction - Muslea, Minton, Knoblock (1999)(Correct)
With the tremendous amount of information that becomes available on the Web on a daily basis, the ability to quickly develop information agents has become a crucial problem. A vital component of any W... / this paper we introduce a new machine learning method for wrapper
A Latent Variable Model for Multivariate Discretization - Stefano Monti (1999)(Correct)
We describe a new method for multivariate
discretization based on the use of a latent
variable model. The method is proposed as
a tool to extend the scope of applicability of
machine learning algorith... / the scope of applicability of machine learning algorithms that handle
Learning Conditional Probabilities from Incomplete Data: An.. - Marco Ramoni (1999)(Correct)
This paper compares three methods --- em algorithm, Gibbs sampling,
and Bound and Collapse (bc) --- to estimate conditional probabilities
from incomplete databases in a controlled experiment. Results ... / central role in a variety of machine learning domain and approaches from
Text Classification by Bootstrapping with Keywords, EM and Shrinkage - McCallum, Nigam (1999)(Correct)
When applying text classification to complex
tasks, it is tedious and expensive
to hand-label the large amounts of training
data necessary for good performance.
This paper presents an alternative appr... / search engines on the Web with machine learning techniques. Our br information learning Learning Machine algorithms networks
1BC: a First-Order Bayesian Classifier - Flach (1999)(Correct)
In this paper we present 1BC, a first-order
Bayesian Classifier. While the propositional
Bayesian Classifier makes the naive Bayes assumption
of statistical independence of atomic
features (one attrib... / Classifier Content Areas machine learning Tracking Number A br and Luc De Raedt. Mining association rules with multiple relations. In
Nonlinear Autoassociation is not Equivalent to PCA - Nathalie Japkowicz (1999)(Correct)
A common misperception within the Neural Network community
is that even with nonlinearities in their hidden layer, autoassociators
trained with Backpropagation are equivalent to linear methods such
as... / U.C. Irvine Repository for Machine Learning. These data were compressed
No Free Lunch for Early Stopping - Cataltepe, Abu-Mostafa, Magdon-Ismail (1999)(Correct)
We show that, with a uniform prior on models having the same
training error, early stopping at some fixed training error above the
training error minimum results in an increase in the expected general... / the effective size of the learning machine as training proceeds.
Classifying Unseen Cases with Many Missing Values - Zijian Zheng (1999)(Correct)
Handling missing attribute values is an important issue for
classifier learning, since missing attribute values in either training data
or test (unseen) data affect the prediction accuracy of learne... / natural domains from the UCI machine learning repository are used.
Information Extraction with HMMs and Shrinkage - Freitag, McCallum (1999)(Correct)
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling time series data, and have been applied with success to many language-related tasks such as part of speech tagging, speech re... / training data. As in many machine learning problems however the lack
Exploratory Mining via Constrained Frequent Set Queries - Ng, Lakshmanan, Han, Mah (1999)(Correct)
Although there have been many studies on data mining,
to date there have been few research prototypes or commercial
systems supporting comprehensive query-driven
mining, which encourages interactive e... / analysis program or a machine learning system should be that the br pushing techniques for mining association rules outlined in and will
Applying Genetic Algorithms to Pronoun Resolution - Byron, Allen (1999)(Correct)
This paper describes a novel technique for resolving pronouns in natural language. A common approach
used in previous studies is to implement a collection of techniques, drawing on both semantic and s... / investigation employing machine learning to try to discover the
Domain-Specific Keyphrase Extraction - Frank, Paynter, Witten (1999)(Correct)
Keyphrases are an important means of document
summarization, clustering, and topic
search. Only a small minority of documents
have author-assigned keyphrases, and manually
assigning keyphrases to exis... / specifically machine learning techniques-are of
Integrating the Evidence Framework and the Support Vector Machine - Kwok (1999)(Correct)
In this paper, we show that training of the support vector
machine (SVM) can be interpreted as performing the level 1 inference
of MacKay's evidence framework. We further on show that levels 2 and
3... / Moreover unlike other machine learning methods SVM's generalization
Tractable Average-Case Analysis of Naive Bayesian Classifiers - Pat Langley (1999)(Correct)
In this paper we present an average-case analysis of the naive Bayesian classifier,
a simple induction algorithm that performs well in many domains. Our
analysis assumes a monotone `M of N' target con... / Most theoretical analyses of machine learning focus on worst-case results
A new Method to index and query Sets - Hoffmann, Koehler (1999)(Correct)
Let us consider the following problem: Given a (probably huge) set of sets S and a query
set q, is there some set s 2 S such that s ` q? This problem occurs in at least three application
areas: the ma... / set is a subset of C. Machine learning is highly concerned with the
A Distributed Solution to the PTE Problem - Giraldez, Elkan, Borrajo (1999)(Correct)
A wide panoply of machine learning methods is available
for application to the Predictive Toxicology Evaluation
(PTE) problem. The authors have built four
monolithic classification systems based on Ti... / Abstract A wide panoply of machine learning methods is available for
Are we better off without Counter-Examples? - Nathalie Japkowicz (1999)(Correct)
Concept-learning is commonly implemented using
discrimination-based techniques which rely on
both examples and counter-examples of the concept.
Recently, however, a recognition-based approach
that le... / of Pattern Recognition Machine Learning Neural Networks and Data
CSPlib: a benchmark library for constraints - Gent, Walsh (1999)(Correct)
We introduce CSPlib, a benchmark library for constraints.
We discuss the advantages and disadvantages of building such a library.
Unlike many other domains (for example, theorem proving, or machine
... / example theorem proving or machine learning representation remains a
Process-Oriented Evaluation: The Next Step - Domingos (1999)(Correct)
Methods to avoid overfitting fall into two
broad categories: data-oriented (using separate
data for validation) and representationoriented
(penalizing complexity in the
model). Both have limitations t... / is a central problem in machine learning and statistics Cheeseman br discovery in empirical learning. Machine Learning - .
A Framework for Programming Embedded Systems: Initial Design and.. - Thrun (1998)(Correct)
This paper describes CES, a proto-type of a new programming language for robots and other
embedded systems, equipped with sensors and actuators. CES contains two new ideas, currently
not found in othe... / intelligence embedded system machine learning mobile robots probabilistic br vision-based reinforcement learning. Machine Learning . A.
Strategy Learning: A Survey Of Problems, Methods, And Architectures - Mehra, Wah (1998)(Correct)
formulations assume that i) the background load at sites does
not change outside the control of PS (no natural dynamics), and ii) E provides
background knowledge relating migration decisions and measu... / learning architectures machine learning sequential problems br issues in temporal difference learning Machine Learning no.
The Omnipresence of Case-Based Reasoning in Science and Application - Aha (1998)(Correct)
A surprisingly large number of research disciplines have contributed towards the
development of knowledge on lazy problem solving, which is characterized by its storage
of ground cases and its demand ... / Lewis Watson machine learning Kolodner b Aha br A theory for memory-based learning. Machine Learning - .
Relational Learning Techniques for Natural Language Information.. - Califf (1998)(Correct)
vii
Chapter 1 Introduction 1
1.1 Organization of Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Chapter 2 Background 5
2.1 Information Extraction . . . . . . . . . .... / have begun to apply machine learning to information extraction br generalization with active learning. Machine Learning
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 . . . . . . . . . . . . . ... / more promising search paths. Machine learning techniques enable a planning br knowledge in inductive learning. Machine Learning - .
Bayesian Model Averaging - Hoeting, Madigan, Raftery, Volinsky (1998)(Correct)
Standard statistical practice ignores model uncertainty. Data analysts typically
select a model from some class of models and then proceed as if the selected model had
generated the data. This approac... / on multiple models from the machine learning neural network br Toward efficient agnostic learning. Machine Learning - .
Maximum Entropy Models For Natural Language Ambiguity Resolution - Ratnaparkhi (1998)(Correct)
This thesis demonstrates that several important kinds of natural language ambiguities can be resolved to state-of-the-art accuracies using a single statistical modeling technique based on the principl... / . . The Machine Learning or Corpus-Based Approach . br is guided by American Bar Association rules or by state bar ethics
Natural Language Grammatical Inference with Recurrent Neural Networks - Lawrence, Giles, Fong (1998)(Correct)
This paper examines the inductive inference of a complex grammar with neural networks -- specifically, the task considered is that of training a network to classify natural language sentences as gramm... / neural networks with other machine learning paradigms on this problem br Combining symbolic and neural learning. Machine Learning -
Generalization-Based Data Mining in Object-Oriented Databases Using.. - Han, Nishio, Kawano, Wang (1998)(Correct)
Data mining is the discovery of knowledge and useful information from the large amounts of data stored in
databases. With the increasing popularity of object-oriented database systems in advanced data... / task in database statistics machine learning and data visualization br rules discriminant rules association rules and classification rules.
Map Learning and High-Speed Navigation in RHINO - Thrun, Bücken, Burgard, Fox.. (1998)(Correct)
This chapter surveys basic methods for learning maps and high speed autonomous navigation for indoor
mobile robots. The methods have been developed in our lab over the past few years, and most of them... / robot RHINO. ffl Learning. Machine learning algorithms are employed to br The robot RHINO. ffl Learning. Machine learning algorithms are
A Machine Learning Approach to POS Tagging - Màrquez, Padró, Rodríguez (1998)(Correct)
We have applied the inductive learning of statistical decision trees and relaxation
labelling to the Natural Language Processing (nlp) task of morphosyntactic disambiguation (Part
Of Speech Tagging)... / in The Netherlands. A Machine Learning Approach to POS Tagging
Intelligent Diagnosis Systems - Balakrishnan, Honavar (1998)(Correct)
This paper examines and compares several different approaches to the design
of intelligent systems for diagnosis and advising applications. These include expert
systems or knowledge-based systems, cas... / -typically using machine learning techniques. As will become br Mathematical Foundations of Learning Machines. Palo Alto CA Morgan
Individual Learning of Coordination Knowledge - Sen, Sekaran (1998)(Correct)
Social agents, both human and computational, inhabiting a world containing
multiple active agents, need to coordinate their activities. This is
because agents share resources, and without proper coord... / composition and dynamics. For machine learning researchers multiagent
Feature Weighting for Lazy Learning Algorithms - Aha (1998)(Correct)
Learning algorithms differ in the degree to which they process their inputs
prior to their use in performance tasks. Many algorithms eagerly compile input
samples and use only the compilations to m... / Lazy learning algorithms are machine learning algorithms Mitchell
On-Line Analytical Mining of Association Rules - Zhu (1998)(Correct)
With wide applications of computers and automated data collection tools, massive
amounts of data have been continuously collected and stored in databases, which
creates an imminent need and great oppo... / is based on the public domain machine learning package MLCMineSet br On-Line Analytical Mining of Association Rules by Hua Zhu B.S.
Generalization - Wah (1998)(Correct)
In this paper, we define the generalization problem, summarize various approaches in
generalization, identify the credit assignment problem, and present the problem and some
solutions in measuring gen... / lead to negative results. Machine learning in an area in artificial br in explanation based learning. Machine Learning pages -
A Tutorial on Support Vector Machines for Pattern Recognition - Burges (1998)(Correct)
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, wo... / International Conference on Machine Learning pages - Bari Italy br of a Pattern Recognition Learning Machine There is a remarkable
An Interpretation of the "Self " From the Dynamical Systems.. - Tani (1998)(Correct)
This study attempts to describe the notion of the "self" using dynamical systems
language based on the results of our robot learning experiments. A neural network
model consisting of multiple modules ... / neural networks and machine learning has made a contribution
The Wrapper Approach - Kohavi, John (1998)(Correct)
In the feature subset selection problem, a learning algorithm is faced with
the problem of selecting a relevant subset of features upon which to focus its
attention, while ignoring the rest. To ach... / INTRODUCTION In supervised machine learning an induction algorithm is
Text-learning and intelligent agents - Mladenic (1998)(Correct)
We present an overview of some work in text-learning through the
prism of the three research questions important for development of textlearning
intelligent agents: what representation is used for doc... / of intelligent agents using machine learning techniques are described
The logic of learning: a brief introduction to Inductive Logic.. - Flach (1998)(Correct)
This paper is intended to provide an introduction to ILP. We will both review some of the established approaches to Horn clause induction (Section 2), and recent work on induction of integrity constra... / been studied extensively by machine learning researchers. The aim of br they attribute dependencies association rules or clauses A possible
Learning Function-Free Horn Expressions - Khardon (1998)(Correct)
The problem of learning universally quantified function free first order Horn
expressions is studied. Several models of learning from equivalence and membership
queries are considered, including the m... / normal form formulas. Machine Learning - . Angluin br Queries and concept learning. Machine Learning
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... / the central problem of machine learning e.g.Cheeseman Oldford br discovery in empirical learning. Machine Learning - .
Inference and Learning in Hybrid Bayesian Networks - Murphy (1998)(Correct)
We survey the literature on methods for inference and learning in Bayesian Networks composed of
discrete and continuous nodes, in which the continuous nodes have a multivariate Gaussian distribution,
... / with hidden variables. Machine Learning . To appear. BSF
Making Use of Population Information in Evolutionary Artificial.. - Yao, Liu (1998)(Correct)
This paper is concerned with the simultaneous evolution
of artificial neural network (ANN) architectures and
weights. The current practice in evolving ANNs is to choose
the best ANN in the last genera... / optimisation problem in the machine learning field. For example back-
Bayes Optimal Instance-Based Learning - Kontkanen, Myllymäki, al. (1998)(Correct)
In this paper we present a probabilistic formalization of the
instance-based learning approach. In our Bayesian framework, moving
from the construction of an explicit hypothesis to a data-driven ins... / Pp. - in Machine Learning ECML- Proceedings of the
Knowledge Discovery Via Multiple Models - Domingos (1998)(Correct)
If it is to qualify as knowledge, a learner's output should be accurate, stable and
comprehensible. Learning multiple models can improve significantly on the accuracy
and stability of single models, b... / applications. Because machine learning seeks to capture a broad br Angluin. Queries and concept learning. Machine Learning - .
Conjectural Equilibrium in Multiagent Learning - Wellman, Hu (1998)(Correct)
Learning in a multiagent environment is complicated by the fact that as other agents learn, the
environment effectively changes. Moreover, other agents' actions are often not directly observable, an... / Machine Learning - c fl
DOGMA: A GA-Based Relational Learner - Hekanaho (1998)(Correct)
We describe a GA-based concept learning/theory revision system DOGMA
and discuss how it can be applied to relational learning. The search for better
theories in DOGMA is guided by a novel fitness func... / applied to a wide range of Machine Learning problems. They work by br algorithms for concept learning. Machine Learning -
A Quantum Computational Learning Algorithm - Ventura, Martinez (1998)(Correct)
An interesting classical result due to Jackson allows polynomial-time
learning of the function class DNF using membership queries. Since in most
practical learning situations access to a membership or... / Martinez Neural Networks and Machine Learning Laboratory
Constructive Theory Refinement in Knowledge Based Neural Networks - Parekh, Honavar (1998)(Correct)
Knowledge based artificial neural networks offer
an approach for connectionist theory refinement. We
present an algorithm for refining and extending the domain
theory incorporated in a knowledge based... / at ftp.cs.wisc.edu machine-learning shavlikgroup datasets br of knowledge in inductive learning Machine Learning vol. pp.
Predicting the Stock Market - Hellström, Holmström (1998)(Correct)
This paper presents a tutorial introduction to predictions of stock time series. The various approaches of technical and fundamental analysis is presented and the prediction problem is formulated as a... / Stock returns Prediction Machine learning Data Mining Bias variance
Stochastic Attribute Selection Committees - Zijian Zheng (1998)(Correct)
Classifier committee learning methods generate multiple classifiers
to form a committee by repeatedly applying a single base learning
algorithm. The committee members vote to decide the final classi... / Boosting is not. Keywords machine learning decision tree learning
Nonlinear Component Analysis as a Kernel Eigenvalue Problem - Schölkopf, Smola, Müller (1998)(Correct)
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high--dim... / was known Burges the machine learning community has made little br and speed of support vector learning machines. In Advances in Neural
Scalable Techniques for Mining Causal Structures - Silverstein, Brin, Motwani, Ullman (1998)(Correct)
Mining for association rules in market basket data has proved a fruitful area of research. Measures
such as conditional probability (confidence) and correlation have been used to infer rules
of the fo... / Networks from Data. Machine Learning pages - . br Abstract Mining for association rules in market basket data has
Exploratory Mining and Pruning Optimizations of Constrained.. - Ng (1998)(Correct)
From the standpoint of supporting human-centered discovery of knowledge, the present-day model of mining association rules suffers from the following serious shortcomings: (i) lack of user exploration... / present-day model of mining association rules suffers from the following
Towards a Standardized Comparison of Search Algorithms - Kainz, Kaindl (1998)(Correct)
Although many search algorithms have been developed and still are under development,
it is difficult to compare them on a fair basis. Theoretical comparisons are
desirable, but it is difficult to make... / Specifically for certain machine learning experiments e.g.certain
Optimization of Constrained Frequent Set Queries with 2-variable.. - Lakshmanan, Ng, Han, Pang (1998)(Correct)
Currently, there is tremendous interest in providing ad-hoc
mining capabilities in database management systems. As a
first step towards this goal, in [15] we proposed an architecture
for supporting co... / analysis program or a machine learning system should be that the br Since the introduction of association rules the development of
Computing with Dynamic Attractors in Neural Networks - Hirsch, Baird (1998)(Correct)
ing from the details of the design, construction, operation and
training method, we view a network as a dynamical system, to be described
mathematically by difference equations or differential equatio... / Neural Computation Machine Learning Cognitive Science Genetic
Pruning Meta-Classifiers in a Distributed Data Mining System - Prodromidis (1998)(Correct)
JAM is a powerful and portable agent-based distributed data mining system that employs meta-learning techniques to integrate a number of independent classifiers (models) derived in parallel from indep... / manner. Introduction Machine learning constitutes a significant
Refining Initial Points for K-Means Clustering - Bradley, Fayyad (1998)(Correct)
Practical approaches to clustering use an iterative
procedure (e.g. K-Means, EM) which converges
to one of numerous local minima. It is known
that these iterative techniques are especially
sensitive t... / in various ways in the machine learning F pattern recognition
Naive Bayesian Classifier Committees - Zheng (1998)(Correct)
The naive Bayesian classifier provides a very simple yet surprisingly
accurate technique for machine learning. Some researchers have
examined extensions to the naive Bayesian classifier that seek to... / accurate technique for machine learning. Some researchers have
Feature Selection with Neural Networks - Leray (1998)(Correct)
Features gathered from the observation of a phenomenon are not all equally informative: some of them may be noisy, correlated or irrelevant. Feature selection aims at selecting a feature set that is r... / to be investigated in the machine learning community which has
Additive Logistic Regression: a Statistical View of Boosting - Friedman (1998)(Correct)
Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be... / Boosting was proposed in the machine learning literature Freund Schapire
Temporal Sequence Learning and Data Reduction for Anomaly Detection - Lane (1998)(Correct)
The anomaly detection problem can be formulated as one of learning to characterize the behaviors of an individual, system, or network in terms of temporal sequences of discrete data. We present an app... / an authorized user. We take a machine learning viewpoint of this problem
Learning regular languages from simple positive examples - Denis (1998)(Correct)
Learning from positive data constitutes an important topic in Grammatical
Inference since it is believed that the acquisition of grammar by children
only needs syntactically correct (i.e. positive) in... / grammatical inference. Machine Learning - . HKY
Segmentation Problems - Kleinberg, Papadimitriou, Raghavan (1998)(Correct)
We introduce and study a novel genre of optimization
problems, which we call segmentation problems. Our
motivation, in part, is the development of a framework
for evaluating certain data mining and cl... / hypergraph transversals and machine learning Proc. PODS pp. br what qualifies as a pattern association rules and correlations
Integrating Association Rule Mining with Relational Database Systems: .. - Sarawagi (1998)(Correct)
Data mining on large data warehouses is becoming increasingly
important. In support of this trend, we consider a
spectrum of architectural alternatives for coupling mining
with database systems. These... / Integrating Association Rule Mining with Relational br rules classification rules association rules etc. The M-SQL
Convolutive Blind Separation of Non-Stationary Sources - Parra, Spence (1998)(Correct)
Acoustic signals recorded simultaneously in a reverberant environment can be described as
sums of differently convolved sources. The task of source separation is to identify the multiple
channels and ... / community but also by machine learning research that has treated the
Data Mining Approaches for Intrusion Detection - Lee, Stolfo (1998)(Correct)
In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns o... / pattern recognition machine learning and database. Several types br that we have implemented the association rules algorithm and the frequent
Active Markov Localization for Mobile Robots - Fox, Burgard, Thrun (1998)(Correct)
Localization is the problem of determining the position of a mobile robot from sensor data. Most existing
localization approaches are passive, i.e., they do not exploit the opportunity to control the ... / such as heuristic search and machine learning the value of active
On Feature Selection: Learning with Exponentially many Irrelevant.. - Ng (1998)(Correct)
We consider feature selection in the "wrapper
" model of feature selection. This typically
involves an NP-hard optimization problem
that is approximated by heuristic search
for a "good" feature subset... / increasing interest in the Machine Learning community. Impressive
Fast Approximate String Matching in a Dictionary - Baeza-Yates, Navarro (1998)(Correct)
A successful technique to search large textual databases
allowing errors relies on an online search in the
vocabulary of the text. To reduce the time of that online
search, we index the vocabulary as ... / or audio databases machine learning image quantization and
Semantic Lexicon Acquisition for Learning Natural Language Interfaces - Thompson, Mooney (1998)(Correct)
This paper describes a system, Wolfie (WOrd Learning
From Interpreted Examples), that acquires a semantic
lexicon from a corpus of sentences paired with representations
of their meaning. The lexicon l... / NLP is a growing area. Using machine learning to help automate the br generalization with active learning. Machine Learning - .
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... / Niblett. The CN algorithm. Machine Learning - . . br mining systems which induce association rules classification rules or
Co-Evolution in the Successful Learning of Backgammon Strategy - Jordan Pollack (1998)(Correct)
Following Tesauro's work on TD-Gammon, we used a 4000 parameter feed-forward
neural network to develop a competitive backgammon evaluation function.
Play proceeds by a roll of the dice, application of... / blair csee.uq.edu.au Machine Learning - . br the goal of a self-organizing learning machine which starts from a minimal
A New Parameter Estimation Method for Gaussian Mixtures - Singer, Warmuth (1998)(Correct)
We describe a new iterative method for parameter estimation of Gaussian mixtures. The
new method is based on a framework developed by Kivinen and Warmuth for supervised online
learning. In contrast to... / of applications in statistics machine learning and data mining see for
Efficient Data Mining for Path Traversal Patterns - Ming-Syan Chen (1998)(Correct)
In this paper, we explore a new data mining capability which involves mining path traversal
patterns in a distributed information providing environment where documents or objects are
linked together t... / Induction of Decision Trees. Machine Learning - . N. br data mining problems is mining association rules For example