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Volumn , Issue , 2013, Pages 32-38

Interpreting individual classifications of hierarchical networks

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; HIERARCHICAL NETWORK; MACHINE-LEARNING; THEORETICAL FOUNDATIONS;

EID: 84885678081     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIDM.2013.6597214     Document Type: Conference Paper
Times cited : (72)

References (13)
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    • 33847275584 scopus 로고    scopus 로고
    • Unsupervised learning of visual features through spike timing dependent plasticity
    • T. Masquelier and S. Thorpe, "Unsupervised learning of visual features through spike timing dependent plasticity," PLoS Comp. Bio., vol. 3, no. 2, 2007.
    • (2007) PLoS Comp. Bio , vol.3 , Issue.2
    • Masquelier, T.1    Thorpe, S.2
  • 7
    • 76749170318 scopus 로고    scopus 로고
    • An efficient explanation of individual classifications using game theory
    • E. Strumbelj and I. Kononenko, "An efficient explanation of individual classifications using game theory," Journal of Machine Learning Research, vol. 11, no. 1, 2010.
    • (2010) Journal of Machine Learning Research , vol.11 , Issue.1
    • Strumbelj, E.1    Kononenko, I.2
  • 9
    • 0033316361 scopus 로고    scopus 로고
    • Hierarchical models of object recognition in cortex
    • M. Riesenhuber and T. Poggio, "Hierarchical models of object recognition in cortex," Nature Neuroscience, vol. 2, no. 11, 1999.
    • (1999) Nature Neuroscience , vol.2 , Issue.11
    • Riesenhuber, M.1    Poggio, T.2
  • 11
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale SVM learning practical
    • B. Scholkopf, C. Burges, and A. Smola, Eds. MIT Press
    • T. Joachims, "Making large-scale SVM learning practical." in Advances in Kernel Methods-Support Vector Learning, B. Scholkopf, C. Burges, and A. Smola, Eds. MIT Press, 1999.
    • (1999) Advances in Kernel Methods-Support Vector Learning
    • Joachims, T.1
  • 12
    • 84932617705 scopus 로고    scopus 로고
    • Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
    • L. Fei-Fei, R. Fergus, and P. Perona, "Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories," in IEEE. CVPR 2004, Workshop on Generative-Model Based Vision, 2004.
    • (2004) IEEE. CVPR 2004, Workshop on Generative-Model Based Vision
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.