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Volumn 40, Issue 10, 2013, Pages 3900-3905

A comparative study of the scalability of a sensitivity-based learning algorithm for artificial neural networks

Author keywords

Algorithms; Classifier design and evaluation; Machine learning; Neural nets

Indexed keywords

CLASSIFIER DESIGN AND EVALUATION; COMMON CRITERIA; COMPARATIVE STUDIES; FEED-FORWARD; LEARNING METHODS; LINEAR SYSTEM OF EQUATIONS; PERFORMANCE OF ALGORITHM; TRAINING ALGORITHMS;

EID: 84875364685     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.12.076     Document Type: Article
Times cited : (3)

References (18)
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    • Stochastic gradient learning in neural networks
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    • Bottou, L. (1991). Stochastic gradient learning in neural networks. In Proceedings of Neuro-Nîmes 91, EC2, Nimes, France.
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    • A parameter-free classification method for large scale learning
    • M. Boullé A parameter-free classification method for large scale learning Journal of Machine Learning Research 10 2009 1367 1385
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    • Boullé, M.1
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    • SVMTorch: Support vector machines for large-scale regression problems
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    • Collobert, R.1    Bengio, S.2
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    • Addison-Wesley Menlo Park, California
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    • Hecht-Nielsen, R.1
  • 12
    • 0027205884 scopus 로고
    • A scaled conjugate gradient algorithm for fast supervised learning
    • M. Møller A scaled conjugate gradient algorithm for fast supervised learning Neural Networks 6 4 1993 525 533
    • (1993) Neural Networks , vol.6 , Issue.4 , pp. 525-533
    • Møller, M.1
  • 13
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    • The Levenberg-Marquardt algorithm: Implementation and theory
    • J. More The Levenberg-Marquardt algorithm: Implementation and theory Numerical Analysis 1978 105 116
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    • More, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.