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.
Inductive Inference of Minimal Programs
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
Learning Conjunctions of Horn Clauses
Exact Identification of Circuits Using Fixed Points of Amplification Functions
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- © Morgan Kaufmann 1990
- 1st August 1990
- Morgan Kaufmann
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- Paperback ISBN: