Knowledge Acquisition and Machine Learning, 1st Edition

Key Features

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* 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

Knowledge Acquisition and Machine Learning, 1st Edition

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.