COLT-Related Publications in Machine Learning

Machine Learning, Volume 34, Numbers 1-3, February 1999,
Special Issue on Machine Learning and Natural Language

Claire Cardie, and Raymond J. Mooney.
Guest Editors' Introduction: Machine Learning and Natural Language.
Machine Learning, Volume 34, No. 1-3, 1999, 5-9.

Walter Daelemans, Antal van den Bosch, and Jakub Zavrel.
Forgetting Exceptions is Harmful in Language Learning.
Machine Learning, Volume 34, No. 1-3, 1999, 11-41.

Ido Dagan, Lillian Lee, and Fernando C. N. Pereira.
Similarity-Based Models of Word Cooccurrence Probabilities.
Machine Learning, Volume 34, No. 1-3, 1999, 43-69.

Michael R. Brent.
An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery.
Machine Learning, Volume 34, No. 1-3, 1999, 71-105.

Andrew R. Golding, and Dan Roth.
A Winnow-Based Approach to Context-Sensitive Spelling Correction.
Machine Learning, Volume 34, No. 1-3, 1999, 107-130.

Masahiko Haruno, Satoshi Shirai, and Yoshifumi Ooyama.
Using Decision Trees to Construct a Practical Parser.
Machine Learning, Volume 34, No. 1-3, 1999, 131-149.

Adwait Ratnaparkhi.
Learning to Parse Natural Language with Maximum Entropy Models.
Machine Learning, Volume 34, No. 1-3, 1999, 151-175.

Doug Beeferman, Adam Berger, and John D. Lafferty.
Statistical Models for Text Segmentation.
Machine Learning, Volume 34, No. 1-3, 1999, 177-210.

Daniel M. Bikel, Richard Schwartz, and Ralph M. Weischedel.
An Algorithm that Learns What's in a Name.
Machine Learning, Volume 34, No. 1-3, 1999, 211-231.

Stephen Soderland.
Learning Information Extraction Rules for Semi-Structured and Free Text.
Machine Learning, Volume 34, No. 1-3, 1999, 233-272.

Machine Learning, Volume 35, Number 1, April 1999

Geoffrey I. Webb, Jason Wells, and Zijian Zheng.
An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition.
Machine Learning, Volume 35, No. 1, 1999, 5-23.

Boris Mirkin.
Concept Learning and Feature Selection Based on Square-Error Clustering.
Machine Learning, Volume 35, No. 1, 1999, 25-39.

David Wolpert, and William G. Macready.
An Efficient Method To Estimate Bagging's Generalization Error.
Machine Learning, Volume 35, No. 1, 1999, 41-55.

Roni Khardon.
Learning to Take Actions.
Machine Learning, Volume 35, No. 1, 1999, 57-90.

Machine Learning, Volume 35, Number 2, May 1999

Roni Khardon, and Dan Roth.
Learning to Reason with a Restricted View.
Machine Learning, Volume 35, No. 2, 1999, 95-116.

Nicolas Meuleau, and Paul Bourgine.
Exploration of Multi-State Environments: Local Measures and Back-Propagation of Uncertainty.
Machine Learning, Volume 35, No. 2, 1999, 117-154.

Eric B. Baum.
Toward a Model of Intelligence as an Economy of Agents.
Machine Learning, Volume 35, No. 2, 1999, 155-185.

Machine Learning, Volume 35, Number 3, June 1999
Special Issue for COLT'97

John Shawe-Taylor.
Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT'97.
Machine Learning, Volume 35, No. 3, 1999, 191-192.

Avrim Blum, and Adam Kalai.
Universal Portfolios With and Without Transaction Costs.
Machine Learning, Volume 35, No. 3, 1999, 193-205.

Víictor Dalmau.
A Dichotomy Theorem for Learning Quantified Boolean Formulas.
Machine Learning, Volume 35, No. 3, 1999, 207-224.

Dimitris Bertsimas, David Gamarnik, and John N. Tsitsiklis.
Estimation of Time-Varying Parameters in Statistical Models: An Optimization Approach.
Machine Learning, Volume 35, No. 3, 1999, 225-245.

V. G. Vovk.
Derandomizing Stochastic Prediction Strategies.
Machine Learning, Volume 35, No. 3, 1999, 247-282.

Machine Learning, Volume 36, Numbers 1-2, July 1999
Special Issue on Integrating Multiple Learned Models.

Philip K. Chan, Salvatore J. Stolfo, and David Wolpert.
Guest Editors' Introduction.
Machine Learning, Volume 36, No. 1-2, 1999, 5-7.

Christopher J. Merz, and Michael J. Pazzani.
A Principal Components Approach to Combining Regression Estimates.
Machine Learning, Volume 36, No. 1-2, 1999, 9-32. Abstract.

Christopher J. Merz.
Using Correspondence Analysis to Combine Classifiers.
Machine Learning, Volume 36, No. 1-2, 1999, 33-58. Abstract.

Padhraic Smyth, and David Wolpert.
Linearly Combining Density Estimators via Stacking.
Machine Learning, Volume 36, No. 1-2, 1999, 59-83.

Leo Breiman.
Pasting Small Votes for Classification in Large Databases and On-Line.
Machine Learning, Volume 36, No. 1-2, 1999, 85-103.

Eric Bauer, and Ron Kohavi.
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants.
Machine Learning, Volume 36, No. 1-2, 1999, 105-139.

Machine Learning, Volume 36, Number 3, September 1999

Peter Auer, and Philip M. Long.
Structural Results About On-line Learning Models With and Without Queries.
Machine Learning, Volume 36, No. 3, 1999, 147-181.

Fernando C. N. Pereira, and Yoram Singer.
An Efficient Extension to Mixture Techniques for Prediction and Decision Trees.
Machine Learning, Volume 36, No. 3, 1999, 183-199.

Tapio Elomaa, and Juho Rousu.
General and Efficient Multisplitting of Numerical Attributes.
Machine Learning, Volume 36, No. 3, 1999, 201-244.

Machine Learning, Volume 37, Number 1, October 1999

Sally A. Goldman, and Stephen D. Scott.
A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns. Machine Learning Volume 37, No. 1, 1999, 5-49.

Steven Hampson, and Dennis Kibler.
Minimum Generalization Via Reflection: A Fast Linear Threshold Learner.
Machine Learning Volume 37, No. 1, 1999, 51-73.

Lawrence K. Saul, and Michael I. Jordan.
Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones.
Machine Learning Volume 37, No. 1, 1999, 75-87.

Carlos Domingo, Nina Mishra, and Leonard Pitt.
Efficient Read-Restricted Monotone CNF/DNF Dualization by Learning with Membership Queries.
Machine Learning Volume 37, No. 1, 1999, 89-110.

Machine Learning, Volume 37, Number 2, November 1999

Leslie G. Valiant.
Projection Learning.
Machine Learning, Volume 37, No. 2, 1999, 115-130.

Michael Schmitt.
On the Sample Complexity for Nonoverlapping Neural Networks.
Machine Learning, Volume 37, No. 2, 1999, 131-141. Abstract.

Leonardo Carbonara, and Derek Sleeman.
Effective and Efficient Knowledge Base Refinement.
Machine Learning, Volume 37, No. 2, 1999, 143-181.

Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul.
An Introduction to Variational Methods for Graphical Models.
Machine Learning, Volume 37, No. 2, 1999, 183-233.

Machine Learning, Volume 37, Number 3, December 1999
Special Issue for COLT'98

Jonathan Baxter, and Nicoḷ Cesa-Bianchi.
Guest Editors Introduction.
Machine Learning, Volume 37, No. 3, 1999, 239-240.

Roni Khardon.
Learning Function-Free Horn Expressions.
Machine Learning, Volume 37, No. 3, 1999, 241-275.

Yoav Freund, and Robert E. Schapire.
Large Margin Classification Using the Perceptron Algorithm.
Machine Learning, Volume 37, No. 3, 1999, 277-296.

Robert E. Schapire, and Yoram Singer.
Improved Boosting Algorithms Using Confidence-rated Predictions.
Machine Learning, Volume 37, No. 3, 1999, 297-336.

Philip M. Long.
The Complexity of Learning According to Two Models of a Drifting Environment.
Machine Learning, Volume 37, No. 3, 1999, 337-354.

David A. McAllester.
Some PAC-Bayesian Theorems.
Machine Learning, Volume 37, No. 3, 1999, 355-363.

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