Machine Learning
An Artificial Intelligence Approach, Volume III
- 1st Edition - August 1, 1990
- Latest edition
- Authors: Yves Kodratoff, Ryszard S. Michalski
- Language: English
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… Read more
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Description
Description
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
Table of contents
Preface
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: June 28, 2014
- Language: English
About the authors
About the authors
YK
Yves Kodratoff
Affiliations and expertise
University Paris-SudRM
Ryszard S. Michalski
Affiliations and expertise
George Mason UniversityView book on ScienceDirect
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