Knowledge Acquisition and Machine Learning
Theory, Methods, and Applications
- 1st Edition - August 2, 1993
- Latest edition
- Authors: Katharina Morik, Stefan Wrobel, Jorg-Uwe Kietz, Werner Emde
- Language: English
For graduate-/research- level students and professors, this book integrates machine learning with knowledge acquisition to overcome the problems of building models for kn… Read more
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Description
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.
Key features
Key features
- Integrates machine learning with knowledge acquisition to overcome the problems of building models for knowledge based systems to maintain them successfully
- Reports on BLIP and MOBAL systems that have been developed over the past 10 years, which illustrate a particular way of unifying knowledge acquisition and machine learning
- Practically oriented--theoretical results have been used and tested in real-world applications from the start
Table of contents
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.
Product details
Product details
- Edition: 1
- Latest edition
- Published: August 2, 1993
- Language: English
About the authors
About the authors
KM
Katharina Morik
Affiliations and expertise
Universitat DortmundSW
Stefan Wrobel
Affiliations and expertise
GMDJK
Jorg-Uwe Kietz
Affiliations and expertise
GMDWE
Werner Emde
Affiliations and expertise
GMD