Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
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.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
ISBN-13
978-0-323-13770-6 (9780323137706)
Schweitzer Klassifikation
Invited Lecture Inductive Inference of Minimal ProgramsTechnical 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 StrategiesShort 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