Machine Learning: An Artificial Intelligence Approach, Band 3

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Yves Kodratoff, Ryszard S. Michalski, Ryszard Stanisław Michalski, Jaime Guillermo Carbonell, Tom Michael Mitchell
Morgan Kaufmann, 1990 - 825 Seiten
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One of the largest and most active areas of AI, machine learning is of interest to students of psychology, philosophy of science, and education. Although self-contained, volume III follows the tradition of volume I (1983) and volume II (1986). Annotation copyrighted by Book News, Inc., Portland, OR
 

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Inhalt

Explanations Machine Learning
31
JeanGabriel Ganascia
49
PART TWO EMPIRICAL LEARNING METHODS
61
E Stepp
103
An ExemplarBased Learning
114
Probabilistic Decision Trees
142
Integrating Quantitative and Qualitative
153
The Operator
191
PART FOUR INTEGRATED LEARNING SYSTEMS
397
Rendell
423
Guiding Induction with Domain Theories
474
Knowledge Base Refinement as Improving
493
Apprenticeship Learning in Imperfect
514
PART FIVE SUBSYMBOLIC AND HETEROGENOUS
553
GeneticAlgorithmbased Learning
611
Applying Valiants Learning Framework
641

Learning Fault Diagnosis Heuristics from
214
PART THREE ANALYTICAL LEARNING METHODS
269
Acquiring General Iterative Concepts
302
Discovering Algorithms from Weak
351
A System that Learns Using
360
Conditional Operationality
383
A New Approach to Unsupervised Learning
670
Bibliography of Recent Machine Learning Research 19851989
685
About the Authors
790
Subject Index
807
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