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Volumn 2714, Issue , 2003, Pages 1159-1167

Recognition of structure classification of protein folding by NN and SVM hierarchical learning architecture

Author keywords

[No Author keywords available]

Indexed keywords

ARCHITECTURE; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; NETWORK ARCHITECTURE; NEURAL NETWORKS; PROTEIN FOLDING; STOCHASTIC SYSTEMS; SUPPORT VECTOR MACHINES;

EID: 35248893366     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44989-2_138     Document Type: Article
Times cited : (41)

References (14)
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  • 4
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  • 5
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    • Dubchak, I.1    Muchnik, I.2    Mayor, C.3    Dralyuk, I.4    Kim, S.H.5
  • 6
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  • 7
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    • Enlarged representative set of protein structures
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    • Hobohm, U.1    Sander, C.2
  • 9
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    • Making large-scale SVM learning practical
    • B. Scholkopf, C. J. C. Burges, and A. J. Smola (editors), Cambridge, MA, MIT Press
    • T. Joachims, "Making large-scale SVM learning practical," in Advances in kernel methods - Support vector learning, B. Scholkopf, C. J. C. Burges, and A. J. Smola (editors), Cambridge, MA, MIT Press, 1998.
    • (1998) Advances in Kernel Methods - Support Vector Learning
    • Joachims, T.1
  • 10
    • 0036505670 scopus 로고    scopus 로고
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    • Moody, J.1    Darken, C.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.