Feature Engineering and Classifier Selection: A Case Study
in Venusian Volcano Detection 
     Lars Asker and Richard Maclin

Robot Learning From Demonstration
     Christopher G. Atkeson and Stefan Schaal

On Learning from Multi-Instance Examples: Empirical Evaluation 
of a Theoretical Approach
     Peter Auer

Using Optimal Dependency-Trees for Combinatorial Optimization:
Learning the Structure of the Search Space
     Shumeet Baluja and Scott Davies

The Canonical Distortion Measure for Vector Quantization and 
Function Approximation
     Jonathan Baxter

FONN: Combining First Order Logic with Connectionist Learning
     Marco Botta, Attilio Giordana, and Roberto Piola

Improving Minority Class Prediction Using Case-Specific 
Feature Weights 
     Claire Cardie and Nicholas Howe

A comparative study of inductive logic programming methods for 
software fault prediction
     William Cohen and Prem Devanbu

Learning Symbolic Prototypes
     Piew Datta and Dennis Kibler

PAC Learning with Constant-Partition Classification Noise and
Applications to Decision Tree Induction
     Scott E. Decatur

Efficient Feature Selection in Conceptual Clustering 
     Mark Devaney and Ashwin Ram

Knowledge Acquisition from Examples Via Multiple Models
     Pedro Domingos

Improving Regressors using Boosting Techniques
     Harris Drucker

Expected Mistake Bound Model for On-Line Reinforcement 
Learning
     Claude-Nicolas Fiechter

Learning Bayesian Networks in the Presence of Missing Values
and Hidden Variables 
     Nir Friedman

Probabilistic Linear Tree
     Joao Gama

A Probabilistic Analysis of the Rocchio Algorithm with 
TFIDF for Text Categorization
     Thorsten Joachims

Reinforcement Learning in POMDPs with Function Approximation
     Hajime Kimura, Kazuteru Miyazaki, Shigenobu Kobayashi

Option Decision Trees with Majority Votes
     Ron Kohavi and Clayton Kunz

Hierarchically classifying documents using very few words
     Daphne Koller and Mehran Sahami

Addressing the Curse of Imbalanced Training Sets: One-Sided 
Selection
     Miroslav Kubat and Stan Matwin

Automatic Rule Acquisition for Spelling Correction
     Lidia Mangu and Eric Brill

Pessimistic Decision Tree Pruning Based on Tree Size
     Yishay Mansour

Pruning Adaptive Boosting
     Dragos Margineantu and Thomas G. Dietterich

On the Decomposition of Polychotomies into Dichotomies
     Eddy Mayoraz and Miguel Moreira

Self-Improving Factory Simulation using Continuous-Time
Average-Reward Reinforcement Learning
     Sridhar Mahadevan, Nicholas Marchalleck, Tapas K. Das,
     and Abhijit Gosavi

ARACHNID: Adaptive Retrieval Agents Choosing Heuristic 
Neighborhoods for Information Discovery
     Filippo Menczer

Efficient Locally Weighted Polynomial Regression 
Predictions
     Andrew W. Moore, Jeff S. Schneider, and Kan Deng

Preventing "Overfitting" of Cross-Validation Data
     Andrew Y. Ng

The Effects of Training Set Size on Decision Tree Complexity
     Tim Oates and David Jensen

The Effective Size of a Neural Network: A Principal Component
Approach
     David Opitz

Exponentiated Gradient Methods for Reinforcement Learning
     Doina Precup and Richard S. Sutton

Learning Goal-Decomposition Rules using Exercises
     Chandra Reddy and Prasad Tadepalli

Learning String Edit Distance
     Eric Sven Ristad and Peter N. Yianilos

An adaptation of Relief for attribute estimation in 
regression
     Marko Robnik-Sikonja and Igor Kononenko

Predicting Multiprocessor Memory Access Patterns with 
Learning Models
     Majd F. Sakr, Steven P. Levitan,  Donald M. Chiarulli,
     Bill G. Horne, and C. Lee Giles

Using output codes to boost multiclass learning problems
     Robert Schapire

Boosting the margin: a new explanation for the effectiveness 
of voting methods
     Robert Schapire, Yoav Freund, Peter Bartlett, and 
     Wee Sun Lee

Why Experimentation can be better than ``Perfect Guidance''
     Tobias Scheffer, Russell Greiner, and Christian Darken

Characterizing the generalization performance of model 
selection strategies
     Dale E. Schuurmans, Lyle H. Ungar, and Dean P. Foster

A Bayesian Approach to Model Learning in Non-Markovian
Environments
     Nobuo Suematsu, Akira Hayashi, and Shigang Li

Hierarchical Explanation-Based Reinforcement Learning
     Prasad Tadepalli and Tom Dietterich

Stacking Bagged and Dagged Models
     Kai Ming Ting and Ian H. Witten

Declarative bias in equation discovery
     Ljupco Todorovski and Saso Dzeroski

Functional Models for Regression Tree Leaves
     Lums Torgo

Integrating Feature Construction with Multiple Classifiers
in Decision Tree Induction
     Ricardo Vilalta and Larry A. Rendell

Instance Pruning Techniques
     D. Randall Wilson and Tony R. Martinez

A Comparative Study on Feature Selection in Text 
Categorization
     Yiming Yang and Jan Pedersen

Machine Learning by Function Decomposition
     Blaz Zupan, Marko Bohanec, Ivan Bratko, and 
     Janez Demsar

Contents