Machine Learning
An Artificial Intelligence Approach
1st Edition
Published on 3. December 2014
836 pages
978-0-08-051055-2 (ISBN)
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.
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E-Book
02/2020
2nd Edition
Academic Press
€91.95
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Additional editions
Yves Kodratoff | Ryszard S. Michalski
Machine Learning
An Artificial Intelligence Approach, Volume III
Book
12/1990
Morgan Kaufmann
€74.46
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Content
PrefacePart One General Issues Chapter 1 Research in Machine Learning; Recent Progress, Classification of Methods, and Future Directions Chapter 2 Explanations, Machine Learning, and CreativityPart 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 ModelsPart 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 GeneralizationPart 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
