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Download Artificial Intelligence and Neural Networks: Steps Toward Principled Integration (Neural Networks: Foundations to Applications) fb2

by Leonard Uhr,Vasant Honavar
Download Artificial Intelligence and Neural Networks: Steps Toward Principled Integration (Neural Networks: Foundations to Applications) fb2
Computer Science
  • Author:
    Leonard Uhr,Vasant Honavar
  • ISBN:
    0123550556
  • ISBN13:
    978-0123550552
  • Genre:
  • Publisher:
    Academic Press (November 24, 1994)
  • Pages:
    653 pages
  • Subcategory:
    Computer Science
  • Language:
  • FB2 format
    1845 kb
  • ePUB format
    1130 kb
  • DJVU format
    1351 kb
  • Rating:
    4.2
  • Votes:
    900
  • Formats:
    mbr lrf azw lit


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FREE shipping on qualifying offers. Traditional artificial intelligence and neural networks are generally considered appropriate for solving different types of problems.

Artificial Intelligence and Neural Networks: Steps Toward Principled Integration Neural Networks Series Neural networks, foundations to applications.

Symbol Processors Versus Connectionist Networks. Representation and Inference

Symbol Processors Versus Connectionist Networks. Representation and Inference. oceedings{ivedA, title {Books-Received - Artificial Intelligence and Neural Networks - Steps Toward Principled Integration}, author {Vasant G Honavar and Leonard Uhr}, year {1994} }. Vasant G Honavar, Leonard Uhr. Published 1994. Symbol Processors Versus Connectionist Networks.

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In: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. The Unified Learning Paradigm: A Foundation for AJ. In: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration

In: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. Honavar, V. and Uhr, L. (E. San Diego, CA: Academic Press. In: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration.

Steps Toward Principled Integration. Neural Networks, Foundations to Applications.

Artificial Intelligence and Neural Networks : Steps Toward Principled Integration. AbeBooks may have this title (opens in new window). Examples of this integration for a variety of specific applications are outlined. The text also provides an introduction to the basics of symbol processing, connectionist networks, and their integration. Format Hardback 664 pages. by Vasant Honavar and Vasant Homavar.

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Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. Leonard Uhr. Algorithm Structured Computer Arrays and Networks: Architectures for Images, Percepts, Models, Information. Boston: Academic Press. New York: Academic Press. Multi-Computer Architectures for Artificial Intelligence: Toward Fast, Robust, Parallel Systems. Leonard Uhr (E. Parallel Computer Vision.

Traditional artificial intelligence and neural networks are generally considered appropriate for solving different types of problems. On the surface, these two approaches appear to be very different, but a growing body of current research is focused on how the strengths of each can be incorporated into the other and built into systems that include the best features of both. Artificial Intelligence and Neural Networks: Steps Toward Principled Integration is a critical examination of the key issues, underlying assumptions, and suggestions related to the reconciliation and principled integration of artificial intelligence and neural networks. With contributions from leading researchers in the field, this comprehensive text provides a thorough introduction to the basics of symbol processing, connectionist networks, and their integration. Numerous examples of the integration of artificial intelligence and neural networks for a variety of specific applications provide unique insight into this evolving area. Includes contributions from some of the leading researchers in this area Provides a complete introduction to the basics of symbol processing, connectionist networks, and their integration Includes examples of the integration of artificial intelligence and neural networks for a variety of specific applications, including vision and pattern recognition