Machine Learning : Proceedings of the Eleventh International Conference

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ISBN: 9781558603356 | 1558603352
Cover: Paperback
Copyright: 6/1/1994

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Supplemental Materials

Preface
Workshops
Tutorials
Organizing Committee
Program Committee
Schedule
A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars	p. 3
Learning Recursive Relations with Randomly Selected Small Training Sets	p. 12
Improving Accuracy of Incorrect Domain Theories	p. 19
Greedy Attribute Selection	p. 28
Using Sampling and Queries to Extract Rules from Trained Neural Networks	p. 37
The Generate, Test, and Explain Discovery System Architecture	p. 46
Boosting and Other Machine Learning Algorithms	p. 53
In Defense of C4.5: Notes on Learning One-Level Decision Trees	p. 62
Incremental Reduced Error Pruning	p. 70
An Incremental Learning Approach for Completable Planning	p. 78
Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains	p. 87
Learning Disjunctive Concepts by Means of Genetic Algorithms	p. 96
Consideration of Risk in Reinforcement Learning	p. 105
Rule Induction for Semantic Query Optimization	p. 112
Irrelevant Features and the Subset Selection Problem	p. 121
An Efficient Subsumption Algorithm for Inductive Logic Programming	p. 130
Getting the Most from Flawed Theories	p. 139
Heterogeneous Uncertainty Sampling for Supervised Learning	p. 148
Markov Games as a Framework for Multi-Agent Reinforcement Learning	p. 157
To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning	p. 164
Comparing Methods for Refining Certainty-Factor Rule-Bases	p. 173
Reward Functions for Accelerated Learning	p. 181
Efficient Algorithms for Minimizing Cross Validation Error	p. 190
Revision of Production System Rule-Bases	p. 199
Using Genetic Search to Refine Knowledge-based Neural Networks	p. 208
Reducing Misclassification Costs	p. 217
Incremental Multi-Step Q-Learning	p. 226
The Minimum Description Length Principle and Categorical Theories	p. 233
Towards a Better Understanding of Memory-based Reasoning Systems	p. 242
Hierarchical Self-Organization in Genetic Programming	p. 251
A Conservation Law for Generalization Performance	p. 259
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms	p. 266
A Constraint-based Induction Algorithm in FOL	p. 275
Learning Without State-Estimation in Partially Observable Markovian Decision Processes	p. 284
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms	p. 293
A Baysian Framework to Integrate Symbolic and Neural Learning	p. 302
A Modular Q-Learning Architecture for Manipulator Task Decomposition	p. 309
An Improved Algorithm for Incremental Induction of Decision Trees	p. 318
A Powerful Heuristic for the Discovery of Complex Patterned Behavior	p. 326
Small Sample Decision Tree Pruning	p. 335
Combining Top-down and Bottom-up Techniques in Inductive Logic Programming	p. 343
Selective Reformulation of Examples in Concept Learning	p. 352
A Statistical Approach to Decision Tree Modeling	p. 363
Bayesian Inductive Logic Programming	p. 371
Frequencies vs Biases: Machine Learning Problems in Natural Language Processing - Abstract	p. 380
Author Index	p. 381
Table of Contents provided by Blackwell. All Rights Reserved.

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