12. ICML 1995:
Tahoe City, California, USA
Armand Prieditis, Stuart J. Russell (Eds.):
Machine Learning, Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, USA, July 9-12, 1995.
Morgan Kaufmann 1995, ISBN 1-55860-377-8 
Contributed Papers
- Naoki Abe, Hang Li, Atsuyoshi Nakamura:
On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms.
3-11
- Hussein Almuallim, Yasuhiro Akiba, Shigeo Kaneda:
On Handling Tree-Structured Attributed in Decision Tree Learning.
12-20
- Peter Auer, Robert C. Holte, Wolfgang Maass:
Theory and Applications of Agnostic PAC-Learning with Small Decision Trees.
21-29
- Leemon C. Baird III:
Residual Algorithms: Reinforcement Learning with Function Approximation.
30-37
- Shumeet Baluja, Rich Caruana:
Removing the Genetics from the Standard Genetic Algorithm.
38-46
- Scott Benson:
Inductive Learning of Reactive Action Models.
47-54
- Justine Blackmore, Risto Miikkulainen:
Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network.
55-63
- Avrim Blum:
Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain.
64-72
- Carla E. Brodley:
Automatic Selection of Split Criterion during Tree Growing Based on Node Location.
73-80
- Clifford Brunk, Michael J. Pazzani:
A Lexical Based Semantic Bias for Theory Revision.
81-89
- Philip K. Chan, Salvatore J. Stolfo:
A Comparative Evaluation of Voting and Meta-learning on Partitioned Data.
90-98
- Pawel Cichosz, Jan J. Mulawka:
Fast and Efficient Reinforcement Learning with Truncated Temporal Differences.
99-107
- John G. Cleary, Leonard E. Trigg:
K*: An Instance-based Learner Using and Entropic Distance Measure.
108-114
- William W. Cohen:
Fast Effective Rule Induction.
115-123
- William W. Cohen:
Text Categorization and Relational Learning.
124-132
- Susan Craw, Paul Hutton:
Protein Folding: Symbolic Refinement Competes with Neural Networks.
133-141
- James Cussens:
A Bayesian Analysis of Algorithms for Learning Finite Functions.
142-149
- Ido Dagan, Sean P. Engelson:
Committee-Based Sampling For Training Probabilistic Classifiers.
150-157
- Piew Datta, Dennis F. Kibler:
Learning Prototypical Concept Descriptions.
158-166
- Gerald DeJong:
A Case Study of Explanation-Based Control.
167-175
- Thomas G. Dietterich, Nicholas S. Flann:
Explanation-Based Learning and Reinforcement Learning: A Unified View.
176-184
- Steven K. Donoho, Larry A. Rendell:
Lessons from Theory Revision Applied to Constructive Induction.
185-193
- James Dougherty, Ron Kohavi, Mehran Sahami:
Supervised and Unsupervised Discretization of Continuous Features.
194-202
- John A. Drakopoulos:
Bounds on the Classification Error of the Nearest Neighbor Rule.
203-208
- Michael O. Duff:
Q-Learning for Bandit Problems.
209-217
- Sean P. Engelson, Moshe Koppel:
Distilling Reliable Information From Unreliable Theories.
218-225
- Philip W. L. Fong:
A Quantitative Study of Hypothesis Selection.
226-234
- Matthias Fuchs:
Learning Proof Heuristics by Adaptive Parameters.
235-243
- Truxton Fulton, Simon Kasif, Steven Salzberg:
Efficient Algorithms for Finding Multi-way Splits for Decision Trees.
244-251
- Luca Maria Gambardella, Marco Dorigo:
Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem.
252-260
- Geoffrey J. Gordon:
Stable Function Approximation in Dynamic Programming.
261-268
- Russell Greiner:
The Challenge of Revising an Impure Theory.
269-277
- Jukka Hekanaho:
Symbiosis in Multimodal Concept Learning.
278-285
- Mark Herbster, Manfred K. Warmuth:
Tracking the Best Expert.
286-294
- Hajime Kimura, Masayuki Yamamura, Shigenobu Kobayashi:
Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward.
295-303
- Ron Kohavi, George H. John:
Autmatic Parameter Selection by Minimizing Estimated Error.
304-312
- Eun Bae Kong, Thomas G. Dietterich:
Error-Correcting Output Coding Corrects Bias and Variance.
313-321
- P. Krishnan, Philip M. Long, Jeffrey Scott Vitter:
Learning to Make Rent-to-Buy Decisions with Systems Applications.
233-330
- Ken Lang:
NewsWeeder: Learning to Filter Netnews.
331-339
- Kevin J. Lang:
Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's.
340-343
- Pat Langley, Karl Pfleger:
Case-Based Acquisition of Place Knowledge.
344-352
- Nick Littlestone:
Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes.
353-361
- Michael L. Littman, Anthony R. Cassandra, Leslie Pack Kaelbling:
Learning Policies for Partially Observable Environments: Scaling Up.
362-370
- David J. Lubinsky:
Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves.
371-377
- Wolfgang Maass, Manfred K. Warmuth:
Efficient Learning with Virtual Threshold Gates.
378-386
- Andrew McCallum:
Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State.
387-395
- David E. Moriarty, Risto Miikkulainen:
Efficient Learning from Delayed Rewards through Symbiotic Evolution.
396-404
- Partha Niyogi:
Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions.
405-412
- Richard Nock, Olivier Gascuel:
On Learning Decision Committees.
413-420
- Arlindo L. Oliveira, Alberto L. Sangiovanni-Vincentelli:
Inferring Reduced Ordered Decision Graphs of Minimum Description Length.
421-429
- Jonathan J. Oliver, David J. Hand:
On Pruning and Averaging Decision Trees.
430-437
- Jing Peng:
Efficient Memory-Based Dynamic Programming.
438-446
- Eduardo Pérez, Larry A. Rendell:
Using Multidimensional Projection to Find Relations.
447-455
- Bernhard Pfahringer:
Compression-Based Discretization of Continuous Attributes.
456-463
- J. Ross Quinlan:
MDL and Categorial Theories (Continued).
464-470
- R. Bharat Rao, Diana F. Gordon, William M. Spears:
For Every Generalization Action, Is There Really an Equal and Opposite Reaction?
471-479
- Marcos Salganicoff, Lyle H. Ungar:
Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices.
480-487
- Jürgen Schmidhuber:
Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability.
488-496
- Moninder Singh, Gregory M. Provan:
A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers.
497-505
- Padhraic Smyth, Alexander G. Gray, Usama M. Fayyad:
Retrofitting Decision Tree Classifiers Using Kernel Density Estimation.
506-514
- Brett Squires, Claude Sammut:
Automatic Speaker Recognition: An Application of Machine Learning.
515-521
- W. Nick Street, Olvi L. Mangasarian, W. H. Wolberg:
An Inductive Learning Approach to Prognostic Prediction.
522-530
- Richard S. Sutton:
TD Models: Modeling the World at a Mixture of Time Scales.
531-539
- Geoffrey G. Towell, Ellen M. Voorhees, Narendra Kumar Gupta, Ben Johnson-Laird:
Learning Collection FUsion Strategies for Information Retrieval.
540-548
- Xuemei Wang:
Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition.
549-557
- Gary M. Weiss:
Learning with Rare Cases and Small Disjuncts.
558-565
- David Wolpert:
Horizonal Generalization.
566-574
- Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz:
Learning Hierarchies from Ambiguous Natural Language Data.
575-583
Invited Talks
- W. Bruce Croft:
Machine Learning and Information Retrieval (Abstract).
587
- David Heckerman:
Learning With Bayesian Networks (Abstract).
588
- Dean Pomerleau:
Learning for Automotive Collision Avoidance and Autonomous Control.
589
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