Machine Learning Proceedings 1994 - 1st Edition - ISBN: 9781558603356, 9781483298184

Machine Learning Proceedings 1994

1st Edition

Proceedings of the Eighth International Conference

Editors: William W. Cohen
eBook ISBN: 9781483298184
Imprint: Morgan Kaufmann
Published Date: 1st July 1994
Page Count: 381
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Description

Machine Learning: Proceedings of the Eleventh International Conference covers the papers presented at the Eleventh International Conference on Machine Learning (ML94), held at New Brunswick, New Jersey on July 10-13, 1994. The book focuses on the processes, methodologies, and approaches involved in machine learning, including inductive logic programming, neural networks, and decision trees.

The selection first offers information on learning recursive relations with randomly selected small training sets; improving accuracy of incorrect domain theories; and using sampling and queries to extract rules from trained neural networks. The text then takes a look at boosting and other machine learning algorithms; an incremental learning approach for completable planning; and learning disjunctive concepts by means of genetic algorithms.

The publication ponders on rule induction for semantic query optimization; irrelevant features and the subset selection problem; and an efficient subsumption algorithm for inductive logic programming. The book also examines Bayesian inductive logic programming; a statistical approach to decision tree modeling; and an improved algorithm for incremental induction of decision trees.

The selection is a dependable source of data for researchers interested in machine learning.

Table of Contents


Preface


Workshops


Tutorials


Organizing Committee


Program Committee


Schedule


Contributed Papers


A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars


Learning Recursive Relations with Randomly Selected Small Training Sets


Improving Accuracy of Incorrect Domain Theories


Greedy Attribute Selection


Using Sampling and Queries to Extract Rules from Trained Neural Networks


The Generate, Test, and Explain Discovery System Architecture


Boosting and Other Machine Learning Algorithms


In Defense of C4.5: Notes on Learning One-Level Decision Trees


Incremental Reduced Error Pruning


An Incremental Learning Approach for Completable Planning


Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains


Learning Disjunctive Concepts by Means of Genetic Algorithms


Consideration of Risk in Reinforcement Learning


Rule Induction for Semantic Query Optimization


Irrelevant Features and the Subset Selection Problem


An Efficient Subsumption Algorithm for Inductive Logic Programming


Getting the Most from Flawed Theories


Heterogeneous Uncertainty Sampling for Supervised Learning


Markov Games as a Framework for Multi-Agent Reinforcement Learning


To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning


Comparing Methods for Refining Certainty-Factor Rule-Bases


Reward Functions for Accelerated Learning


Efficient Algorithms for Minimizing Cross Validation Error


Revision of Production System Rule-Bases


Using Genetic Search to Refine Knowledge-Based Neural Networks


Reducing Misc

Details

No. of pages:
381
Language:
English
Copyright:
© Morgan Kaufmann 1994
Published:
Imprint:
Morgan Kaufmann
eBook ISBN:
9781483298184

About the Editor

William W. Cohen

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