COLT Proceedings 1990

COLT Proceedings 1990

1st Edition - August 1, 1990

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  • Editor: COLT
  • eBook ISBN: 9780323137706

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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 Distribution-Free Learning of Probabilistic Concepts

    On Threshold Circuits for Parity

    On the Complexity of Learning from Counterexamples and Membership Queries

    Robust Separations in Inductive Inference

    Separating PAC and Mistake-Bound Learning Models Over the Boolean Domain

    Towards a DNA Sequencing Theory (Learning a String)

    Author Index

Product details

  • No. of pages: 404
  • Language: English
  • Copyright: © Morgan Kaufmann 1990
  • Published: August 1, 1990
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9780323137706

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