Knowledge Acquisition and Machine Learning
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Knowledge Acquisition and Machine Learning : Theory, Methods, and Applications

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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.
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Product details

  • Hardback | 305 pages
  • 154 x 228 x 24mm | 639.58g
  • Academic Press Inc
  • San Diego, United States
  • English
  • 0125062303
  • 9780125062305

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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About Katharina Morik

By Katharina Morik, Stefan Wrobel, Jorg-Uwe Kietz, and Werner Emde
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