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COLT '89
Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989
- 1st Edition - December 25, 1989
- Editor: COLT
- Language: English
- Paperback ISBN:9 7 8 - 1 - 5 5 8 6 0 - 0 8 6 - 7
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 9 4 8 2 9 - 4
Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept… Read more
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Request a sales quoteComputational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition. Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes. This book is a valuable resource for students and teachers.
Foreword
Invited Lecture
Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures)
Technical Papers
Polynomial Learnability of Semilinear Sets
Learning Nested Differences of Intersection-Closed Concept Classes
A Polynomial-Time Algorithm for Learning k-Variable Pattern Languages from Examples
On Learning from Exercises
On Approximate Truth
Informed Parsimonious Inference of Prototypical Genetic Sequences
Complexity Issues in Learning by Neural Nets
Equivalence Queries and Approximate Fingerprints
Learning Read-Once Formulas Using Membership Queries
Learning Simple Deterministic Languages
Learning in the Presence of Inaccurate Information
Convergence to Nearly Minimal Size Grammars by Vacillating Learning Machines
Inductive Inference with Bounded Number of Mind Changes
Learning Via Queries to an Oracle
Learning Structure from Data: A Survey
A Statistical Approach to Learning and Generalization in Layered Neural Networks
The Light Bulb Problem
From On-Line to Batch Learning
A Parametrization Scheme for Classifying Models of Learnability
On the Role of Search for Learning
Elementary Formal System as a Unifying Framework for Language Learning
Identification of Unions of Languages Drawn from an Identifiable Class
Induction from the General to the More General
Space-Bounded Learning and the Vapnik-Chervonenkis Dimension
Reliable and Useful Learning
Short Abstracts
The Strength of Weak Learnability
On the Complexity of Learning form Counterexamples
Generalizing the PAC Model: Sample Size Bounds From Metric Dimension-Based Uniform Convergence Results
A Theory of Learning Simple Concepts Under Simple Distributions
Learning Binary Relations and Total Orders
The Weighted Majority Algorithm
Author Index
- No. of pages: 389
- Language: English
- Edition: 1
- Published: December 25, 1989
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9781558600867
- eBook ISBN: 9780080948294
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