COLT Proceedings 1990

1st Edition

Editors: COLT
Paperback ISBN: 9781558601468
eBook ISBN: 9780323137706
Imprint: Morgan Kaufmann
Published Date: 1st August 1990
Page Count: 404
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Description

COLT '90 covers the proceedings of the Third Annual Workshop on Computational Learning Theory, sponsored by the ACM SIGACT/SIGART, University of Rochester, Rochester, New York on August 6-8, 1990. The book focuses on the processes, methodologies, principles, and approaches involved in computational learning theory.

The selection first elaborates on inductive inference of minimal programs, learning switch configurations, computational complexity of approximating distributions by probabilistic automata, and a learning criterion for stochastic rules. The text then takes a look at inductive identification of pattern languages with restricted substitutions, learning ring-sum-expansions, sample complexity of PAC-learning using random and chosen examples, and some problems of learning with an Oracle.

The book examines a mechanical method of successful scientific inquiry, boosting a weak learning algorithm by majority, and learning by distances. Discussions focus on the relation to PAC learnability, majority-vote game, boosting a weak learner by majority vote, and a paradigm of scientific inquiry.

The selection is a dependable source of data for researchers interested in the computational learning theory.

Table of Contents


Invited Lecture


Inductive Inference of Minimal Programs


Technical Papers


Identifying μ-Formula Decision Trees with Queries


Learning Switch Configurations


On the Computational Complexity of Approximating Distributions by Probabilistic Automata


A Learning Criterion for Stochastic Rules


On the Complexity of Learning Minimum Time-Bounded Turing Machines


Inductive Inference from Positive Data is Powerful


Inductive Indentification of Pattern Languages with Restricted Substitutions


Pattern Languages Are Not Learnable


On Learning Ring-Sum-Expansions


Learning Functions of k Terms


On the Sample Complexity of Pac-Learning Using Random and Chosen Examples


Finite Learning by a "Team"


Some Problems of Learning with an Oracle


A Mechanical Method of Successful Scientific Inquiry


Boosting a Weak Learning Algorithm by Majority


On the Sample Complexity of Weak Learning


Learning by Distances


The Learnability of Formal Concepts


Polynomial Time Algorithms for Learning Neural Nets


Composite Geometric Concepts and Polynomial Predictability


Learning Integer Lattices


On the Number of Examples and Stages Needed for Learning Decision Trees


Learning DNF Under the Uniform Distribution in Quasi-Polynomial Time


Learning Via Queries with Teams and Anomalies


Learning Via Queries in [+,<]


On the Sample Complexity of Finding Good Search Strategies


Inferring Graphs from Walks


Aggregating Strategies


Short Abstracts


Learning Conjunctions of Horn Clauses


Exact Identification of Circuits Using Fixed Points of Amplification Functions


Efficient Di

Details

No. of pages:
404
Language:
English
Copyright:
© Morgan Kaufmann 1990
Published:
Imprint:
Morgan Kaufmann
eBook ISBN:
9780323137706
Paperback ISBN:
9781558601468

About the Editor

Reviews

COLT '90 covers the proceedings of the Third Annual Workshop on Computational Learning Theory, sponsored by the ACM SIGACT/SIGART, University of Rochester, Rochester, New York on August 6-8, 1990. The book focuses on the processes, methodologies, principles, and approaches involved in computational learning theory. The selection first elaborates on inductive inference of minimal programs, learning switch configurations, computational complexity of approximating distributions by probabilistic automata, and a learning criterion for stochastic rules. The text then takes a look at inductive identification of pattern languages with restricted substitutions, learning ring-sum-expansions, sample complexity of PAC-learning using random and chosen examples, and some problems of learning with an Oracle. The book examines a mechanical method of successful scientific inquiry, boosting a weak learning algorithm by majority, and learning by distances. Discussions focus on the relation to PAC learnability, majority-vote game, boosting a weak learner by majority vote, and a paradigm of scientific inquiry. The selection is a dependable source of data for researchers interested in the computational learning theory.