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Machine Learning Proceedings 1991

Proceedings of the Eighth International Workshop (ML91)

  • 1st Edition - June 1, 1991
  • Editors: Lawrence A. Birnbaum, Gregg C. Collins
  • Language: English
  • eBook ISBN:
    9 7 8 - 1 - 4 8 3 2 - 9 8 1 7 - 7

Machine Learning: Proceedings of the Eighth International Workshop (ML91) covers the papers presented at ML91, the Eighth International Workshop on Machine Learning, held at… Read more

Machine Learning Proceedings 1991

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Machine Learning: Proceedings of the Eighth International Workshop (ML91) covers the papers presented at ML91, the Eighth International Workshop on Machine Learning, held at Northwestern University, Evanston, Illinois, USA, in June 1991. The book focuses on constructive induction, learning from theory and data, automated knowledge acquisition, learning in intelligent information retrieval, machine learning in engineering automation, computational models of human learning, and learning reaction strategies. The selection first offers information on design rationale capture as knowledge acquisition, a domain-independent framework for effective experimentation in planning, and knowledge refinement using a high-level, non-technical vocabulary. The text then elaborates on improving the performance of inconsistent knowledge bases via combined optimization method, flexibility of speculative refinement, and a prototype based symbolic concept learning system. Topics include using task descriptions to generate error candidates, functional descriptions of knowledge-based systems, combined optimization method, and inconsistency and related work. The book ponders on learning words from context, modeling the acquisition and improvement of motor skills, a computational model of acquisition for children's addition strategies, and computer modeling of acquisition orders in child language. The manuscript also takes a look at knowledge acquisition combining analytical and empirical techniques; designing integrated learning systems for engineering design; and machine learning for nondestructive evaluation. The selection is highly recommended for researchers interested in machine learning.