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Volumn 22, Issue 1-3, 1986, Pages 187-204

The immune system, adaptation, and machine learning

Author keywords

[No Author keywords available]

Indexed keywords


EID: 46149127936     PISSN: 01672789     EISSN: None     Source Type: Journal    
DOI: 10.1016/0167-2789(86)90240-X     Document Type: Article
Times cited : (1195)

References (28)
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    • J.H. Holland, “Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-base systems,” in: Machine Learning 2, R.S. Michalski, J.G. Carbonell and T.M. Mitchell, eds. (Morgan Kauffman, Los Altos, in press), chap. 20.
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    • Theoretical studies of clonal selection: Minimal antibody repertoire size and reliability of self-non-self discrimination
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  • 8
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    • Incorporation of a nonspecific T cell dependent helper factor into a network theory of the regulation of the immune response
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    • J.D. Farmer, N. Packard and A.S Perelson, in preparation.
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    • The network idea and the immune response
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    • S.A. Kauffman, “Autocatalytic sets of proteins,” J. Theoret. Biol., in press.
  • 23
    • 0003644437 scopus 로고
    • Computer Aided Gas Pipeline Operation Using Genetic Algorithms and Rule Learning
    • 2nd. ed, University of Michigan
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  • 24
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    • A Learning System Based on Genetic Adaptive Algorithms
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  • 27
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    • Some similarities in the way the immune system and nervous system work
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  • 28
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    • G.W. Hoffman, “A neural network model based on the analogy with the immune system,” J. Theoret. Biol., submitted.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.