Knowledge Acquisition and Machine Learning : Theory, Methods, and Applications
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.
- Hardback | 305 pages
- 154 x 228 x 24mm | 639.58g
- 27 Sep 1993
- Elsevier Science Publishing Co Inc
- Academic Press Inc
- San Diego, United States
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.
About Katharina Morik
By Katharina Morik, Stefan Wrobel, Jorg-Uwe Kietz, and Werner Emde