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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

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

  • 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

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

  • Edition: 1
  • Latest edition
  • Published: August 2, 1993
  • Language: English

About the authors

KM

Katharina Morik

Affiliations and expertise
Universitat Dortmund

SW

Stefan Wrobel

Affiliations and expertise
GMD

JK

Jorg-Uwe Kietz

Affiliations and expertise
GMD

WE

Werner Emde

Affiliations and expertise
GMD