Computational Learning Theory and Natural Learning Systems Vol. 2 : Intersections Between Theory and Experiment

Hanson, Petsche, Kearns & Rivest

ISBN 10: 0262581337 / ISBN 13: 9780262581332

Published by MIT Press Ltd, 1994

About this Item

Paperback. Covers shelf-worn and curled. Page block stained. Contents clean.

Seller Inventory # 163778

Bibliographic Details

• Title: Computational Learning Theory and Natural Learning Systems Vol. 2 : Intersections Between Theory and Experiment
• Publisher: MIT Press Ltd
• Publication Date: 1994
• Language: English
• ISBN 10: 0262581337
• ISBN 13: 9780262581332
• Binding: Paperback
• Condition: Very Good
• Dust Jacket Condition: No Dust Jacket
• Seller catalogs:
  • Computers & Technology (incl. AI)

About this title

Synopsis

As with Volume I, this second volume represents a synthesis of issues in three historically distinct areas of learning research: computational learning theory, neural network research, and symbolic machine learning. While the first volume provided a forum for building a science of computational learning across fields, this volume attempts to define plausible areas of joint research: the contributions are concerned with finding constraints for theory while at the same time interpreting theoretic results in the context of experiments with actual learning systems. Subsequent volumes will focus on areas identified as research opportunities.

Computational learning theory, neural networks, and AI machine learning appear to be disparate fields; in fact they have the same goal: to build a machine or program that can learn from its environment. Accordingly, many of the papers in this volume deal with the problem of learning from examples. In particular, they are intended to encourage discussion between those trying to build learning algorithms (for instance, algorithms addressed by learning theoretic analyses are quite different from those used by neural network or machine-learning researchers) and those trying to analyze them.

The first section provides theoretical explanations for the learning systems addressed, the second section focuses on issues in model selection and inductive bias, the third section presents new learning algorithms, the fourth section explores the dynamics of learning in feedforward neural networks, and the final section focuses on the application of learning algorithms.

A Bradford Book

About the Author

Stephen J. Hanson heads the Learning Systems Department at Siemens Corporate Research and is a Visiting Member of the Research Staff and Research Collaborator at the Cognitive Science Laboratory at Princeton University. Thomas Petsche, also at Siemens, is Senior Member of the Technical Staff. Ronald L. Rivest is Professor of Computer Science and Associate Director of the Laboratory for Computer Science at the Massachusetts Institute of Technology. Michael Kearns is Member of the Technical Staff at AT&T Bell Laboratories.