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Machine Learning - 1st Edition - ISBN: 9780080510552

Machine Learning

1st Edition

An Artificial Intelligence Approach, Volume III

Authors: Yves Kodratoff Ryszard Michalski
eBook ISBN: 9780080510552
Imprint: Morgan Kaufmann
Published Date: 1st August 1990
Page Count: 825
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Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.

Table of Contents


Part One General Issues

Chapter 1 Research in Machine Learning; Recent Progress, Classification of Methods, and Future Directions

Chapter 2 Explanations, Machine Learning, and Creativity

Part Two Empirical Learning Methods

Chapter 3 Learning Flexible Concepts: Fundamental Ideas and a Method Bases on Two-Tiered Representation

Chapter 4 Protos: An Exemplar-Based Learning Apprentice

Chapter 5 Probabilistic Decision Trees

Chapter 6 Integrating Quantitative and Qualitative Discovery in the ABACUS System

Chapter 7 Learning by Experimentation: The Operator Refinement Method

Chapter 8 Learning Fault Diagnosis Heuristics from Device Descriptions

Chapter 9 Conceptual Clustering and Categorization: Bridging the Gap between Induction and Causal Models

Part Three Analytical Learning Methods

Chapter 10 LEAP: A Learning Apprentice System for VLSI Design

Chapter 11 Acquiring General Iterative Concepts by Reformulating Explanations of Observed Examples

Chapter 12 Discovering Algorithms from Weak Methods

Chapter 13 OGUST: A System That Learns Using Domain Properties Expressed as Theorems

Chapter 14 Conditional Operationality and Explanation-based Generalization

Part Four Integrated Learning Systems

Chapter 15 The Utility of Similarity-based Learning in a World Needing Explanation

Chapter 16 Learning Expert Knowledge by Improving the Explanations Provided by the System

Chapter 17 Guiding Induction with Domain Theories

Chapter 18 Knowledge Base Refinement as Improving an Incorrect and Incomplete Domain Theory

Chapter 19 Apprenticeship Learning in Imperfect Domain Theories

Part Five Subsymbolic and Heterogenous Learning Systems

Chapter 20 Connectionist Learning Procedures

Chapter 21 Genetic-Algorithm-based Learning

Part Six Formal Analysis

Chapter 22 Applying Valiant's Learning Framework to AI Concept-Learning Problems

Chapter 23 A New Approach to Unsupervised Learning in Deterministic Environments Bibliography of Recent Machine Learning Research (1985-1989)

About the Authors

Author Index

Subject Index


No. of pages:
© Morgan Kaufmann 2014
1st August 1990
Morgan Kaufmann
eBook ISBN:

About the Authors

Yves Kodratoff

Affiliations and Expertise

University Paris-Sud

Ryszard Michalski

Affiliations and Expertise

George Mason University

Ratings and Reviews