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Volumn 4087 LNAI, Issue , 2006, Pages 221-232

Object detection and feature base learning with sparse convolutional neural networks

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; DATA MINING; FEATURE EXTRACTION; LEARNING SYSTEMS; MATHEMATICAL MODELS; OBJECT RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; REAL TIME SYSTEMS; VIDEO SIGNAL PROCESSING;

EID: 33749389929     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11829898_20     Document Type: Conference Paper
Times cited : (7)

References (12)
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    • Convolutional face finder: A neural architecture for fast and robust face detection
    • November
    • G. Garcia and M. Delakis. Convolutional face finder: A neural architecture for fast and robust face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11):1408-1423, November 2004.
    • (2004) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.26 , Issue.11 , pp. 1408-1423
    • Garcia, G.1    Delakis, M.2
  • 4
    • 0032629347 scopus 로고    scopus 로고
    • Fast and robust fixed-point algorithms for independent component analysis
    • A. Hyvärinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10:626-634, 1999.
    • (1999) IEEE Transactions on Neural Networks , vol.10 , pp. 626-634
    • Hyvärinen, A.1
  • 5
    • 0037238922 scopus 로고    scopus 로고
    • Empirical evaluation of the improved Rprop learning algorithm
    • C. Igel and M. Hüsken. Empirical evaluation of the improved Rprop learning algorithm. Neurocomputing, 50(C):105-123, 2003.
    • (2003) Neurocomputing , vol.50 , Issue.C , pp. 105-123
    • Igel, C.1    Hüsken, M.2
  • 7
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proc. IEEE, 86(11):2278-2324, 1998.
    • (1998) Proc. IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 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, 2(11):1019-1025, 1999.
    • (1999) Nature Neuroscience , vol.2 , Issue.11 , pp. 1019-1025
    • Riesenhuber, M.1    Poggio, T.2
  • 10
    • 0002632217 scopus 로고    scopus 로고
    • The SNoW learning architecture
    • UIUC Computer Science Department, May
    • D. Roth. The SNoW learning architecture. Technical Report UIUCDCS-R-99-2101, UIUC Computer Science Department, May 1999.
    • (1999) Technical Report , vol.UIUCDCS-R-99-2101
    • Roth, D.1
  • 12
    • 20444370092 scopus 로고    scopus 로고
    • Unsupervised learning of combination features for hierarchical recognition models
    • H. Wersing and E. Körner. Unsupervised learning of combination features for hierarchical recognition models. In Proceedings of the ICANN, 2002.
    • (2002) Proceedings of the ICANN
    • Wersing, H.1    Körner, E.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.