Knowledge Acquisition and Machine Learning
Theory, Methods, and Applications
- 1st Edition - September 13, 1993
- Latest edition
- Authors: Katharina Morik, Stefan Wrobel, Jorg-Uwe Kietz, Werner Emde
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 5 0 6 2 3 0 - 5
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 9 2 4 8 9 - 2
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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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.
- 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
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.
- Edition: 1
- Latest edition
- Published: September 13, 1993
- Language: English
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