Machine Learning Proceedings 1988 - 1st Edition - ISBN: 9780934613644, 9781483297699

Machine Learning Proceedings 1988

1st Edition

Editors: John Laird
eBook ISBN: 9781483297699
Imprint: Morgan Kaufmann
Published Date: 25th December 1988
Page Count: 467
Sales tax will be calculated at check-out Price includes VAT/GST
Price includes VAT/GST
× DRM-Free

Easy - Download and start reading immediately. There’s no activation process to access eBooks; all eBooks are fully searchable, and enabled for copying, pasting, and printing.

Flexible - Read on multiple operating systems and devices. Easily read eBooks on smart phones, computers, or any eBook readers, including Kindle.

Open - Buy once, receive and download all available eBook formats, including PDF, EPUB, and Mobi (for Kindle).

Institutional Access

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.


Proceedings of the Fifth International Conference on Machine Learning provides careful theoretical analyses that make clear contact with traditional problems in machine learning. This book discusses the key role of learning in cognition.

Organized into 10 parts encompassing 49 chapters, this book begins with an overview of the OTIS induction system that learns concepts from positive and negative examples by searching through the space of possible concept descriptions. This text then reviews the methods to selecting examples, and explores the ramifications of one in detail. Other chapters consider a reported phenomenon in machine concept learning wherein concept descriptions can be simplified with little ill-effect on classification accuracy. This book discusses as well an implemented system that learns structural models of shape from noisy image data. The final chapter provides a discussion of the relationship between learning and forgetting.

This book is a valuable resource for psychologists, scientists, theorists, and research workers.

Table of Contents

Empirical Learning

Using a Generalization Hierarchy to Learn from Examples

Tuning Rule-Based Systems to their Environments

On Asking the Right Questions

Concept Simplification and Prediction Accuracy

Learning Graph Models of Shape

Learning Categorical Decision Criteria in Biomedical Domains

Conceptual Clumping of Binary Vectors with Occam's Razor

AutoClass: A Bayesian Classification System

Incremental Multiple Concept Learning Using Experiments

Trading Off Simplicity and Coverage in Incremental Concept Learning

Deferred Commitment in UNIMEM: Waiting to Learn

Experiments on the Costs and Benefits of Windowing in ID3

Improved Decision Trees: A Generalized Version of ID3

ID5: An Incremental ID3

Using Weighted Networks to Represent Classification Knowledge in Noisy Domains

Genetic Learning

An Empirical Comparison of Genetic and Decision-Tree Classifiers

Population Size in Classifier Systems

Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms

Classifier Systems with Hamming Weights

Midgard: A Genetic Approach to Adaptive Load Balancing for Distributed Systems

Connectionist Learning

Some Interesting Properties of a Connectionist Inductive Learning System

Competitive Reinforcement Learning

Connectionist Learning of Expert Backgammon Evaluations

Building and Using Mental Models in a Sensory-Motor Domain: A Connectionist Approach

Explanation-Based Learning

Reasoning about Operationality for Explanation-Based Learning

Boundaries of Operationality

On the Tractability of Learning from Incomplete Theories

Active Explanation Reduction: An


No. of pages:
© Morgan Kaufmann 1988
Morgan Kaufmann
eBook ISBN:

About the Editor

John Laird

Affiliations and Expertise

University of Michigan, Ann Arbor, U.S.A.

Ratings and Reviews