Knowledge acquisition and machine learning : theory, methods, and applications
Author(s)
Bibliographic Information
Knowledge acquisition and machine learning : theory, methods, and applications
Academic Press, c1933 [i.e. c1993]
Available at / 19 libraries
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
MOR||44||1(K)93045849
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Note
Bibliography: p. 281-294
Includes indexes
Author: Katharina Morik, Stefan Wrobel, Jörg-Uwe Kietz and Werner Emde
Description and Table of Contents
Description
For graduate-/research- level students and professors, this book integrates machine learning with knowledge acquisition to overcome the problems of building models for knowledge-based systems to maintain them successfully. It also reports on BLIP and MOBAL systems developed over the last decade, which illustrate a particular way of unifying knowledge acquisition and machine learning. Practically-orientated, theoretical skills have been used and tested in real-world applications.
Table of Contents
The Knowledge Acquisition Framework. The Knowledge Representation Environment. The Inference Im-2. The Sort Taxonomy. The Predicate Structure. Model-Driven Rule Discovery. Knowledge Revision. Concept Formation. Practical Experiences. Bibliography. Author Index. Name Index. Subject Index.
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