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Volumn , Issue , 1997, Pages 807-813

Effective training of a neural network character classifier for word recognition

Author keywords

[No Author keywords available]

Indexed keywords

CHARACTER RECOGNITION; COMPUTATIONAL LINGUISTICS; MICROCOMPUTERS; NEURAL NETWORKS;

EID: 56149104083     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (105)

References (15)
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    • Using voice transformations to create additional training talkers for word spotting
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    • E. I. Chang and R. P. Lippmann, "Using Voice Transformations to Create Additional Training Talkers for Word Spotting," in Advances in Neural Information Processing Systems 7, Tesauro et al. (eds.), pp. 875-882, MIT Press, 1995.
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    • Chang, E.I.1    Lippmann, R.P.2
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    • Gish, H.1
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    • (1992) From Pixels to Features III , pp. 493-506
    • Guyon, I.1    Henderson, D.2    Albrecht, P.3    Lecun, Y.4    Denker, P.5
  • 7
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    • Neural networks, bayesian a posteriori probabilities, and pattern classification
    • V. Cherkassky, J. H. Friedman, and H. Wechsler (eds.), Springer- Verlag, Berlin
    • R. P. Lippmann, "Neural Networks, Bayesian a posteriori Probabilities, and Pattern Classification," pp. 83-104 in: From Statistics to Neural Networks-Theory and Pattern Recognition Applications, V. Cherkassky, J. H. Friedman, and H. Wechsler (eds.), Springer-Verlag, Berlin, 1994.
    • (1994) From Statistics to Neural Networks-Theory and Pattern Recognition Applications , pp. 83-104
    • Lippmann, R.P.1
  • 8
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    • Continuous speech recognition-an introduction to the hybrid hmm/connectionist approach
    • May
    • N. Morgan and H. Bourlard, "Continuous Speech Recognition-An introduction to the hybrid HMM/connectionist approach," IEEE Signal Processing Mag., Vol. 13, no. 3, pp. 24-42, May 1995.
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  • 11
    • 0001595997 scopus 로고
    • Neural network classifiers estimate bayesian a posteriori probabilities
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    • Richard, M.D.1    Lippmann, R.P.2
  • 12
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    • Tangent prop-A formalism for specifying selected invariances in an adaptive network
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    • P. Simard, B. Victorri, Y. LeCun and .1. Denker, "Tangent Prop-A Formalism for Specifying Selected Invariances in an Adaptive Network," in Advances in Neural Information Processing Systems 4, Moody et al. (eds.), pp. 895-903, Morgan Kaufmann, 1992.
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  • 13
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    • P. Simard, Y. LeCun and J. Denker, "Efficient Pattern Recognition Using a New Transformation Distance," in Advances in Neural Information Processing Systems 5, Hanson et al. (eds.), pp. 50-58, Morgan Kaufmann, 1993.
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    • Tappert, C.C.1    Suen, C.Y.2    Wakahara, T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.