ICML 1997

48 papers

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

A Comparative Study of Inductive Logic Programming Methods for Software Fault Prediction William W. Cohen, Premkumar T. Devanbu

A Comparative Study on Feature Selection in Text Categorization Yiming Yang, Jan O. Pedersen

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

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

An Adaptation of Relief for Attribute Estimation in Regression Marko Robnik-Sikonja, Igor Kononenko

ARCCHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods Filippo Menczer

Automatic Rule Acquisition for Spelling Correction Lidia Mangu, Eric Brill

Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee

Characterizing the Generalization Performance of Model Selection Strategies Dale Schuurmans, Lyle H. Ungar, Dean P. Foster

Declarative Bias in Equation Discovery Ljupco Todorovski, Saso Dzeroski

Efficient Feature Selection in Conceptual Clustering Mark Devaney, Ashwin Ram

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

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

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

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

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

Functional Models for Regression Tree Leaves Luís Torgo

Hierarchical Explanation-Based Reinforcement Learning Prasad Tadepalli, Thomas G. Dietterich

Hierarchically Classifying Documents Using Very Few Words Daphne Koller, Mehran Sahami

Improving Minority Class Prediction Using Case-Specific Feature Weights Claire Cardie, Nicholas Nowe

Improving Regressors Using Boosting Techniques Harris Drucker

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

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

Knowledge Acquisition Form Examples Vis Multiple Models Pedro M. Domingos

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

Learning Goal-Decomposition Rules Using Exercises Chandra Reddy, Prasad Tadepalli

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

Learning Symbolic Prototypes Piew Datta, Dennis F. Kibler

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

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

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

Option Decision Trees with Majority Votes Ron Kohavi, Clayton Kunz

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

Pessimistic Decision Tree Pruning Based Continuous-Time Yishay Mansour

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

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

Probabilistic Linear Tree João Gama

Pruning Adaptive Boosting Dragos D. Margineantu, Thomas G. Dietterich

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

Robot Learning from Demonstration Christopher G. Atkeson, Stefan Schaal

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

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

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

The Effects of Training Set Size on Decision Tree Complexity Tim Oates, David D. Jensen

Using Optimal Dependency-Trees for Combinational Optimization Shumeet Baluja, Scott Davies

Using Output Codes to Boost Multiclass Learning Problems Robert E. Schapire

Why Experimentation Can Be Better than "Perfect Guidance" Tobias Scheffer, Russell Greiner, Christian Darken