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Volumn 2, Issue , 2007, Pages 275-282

A Bayesian divergence prior for classifier adaptation

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTATION STRATEGIES; ERROR BOUND; GENERIC CLASSIFIER; STATISTICAL CLASSIFIER; TRAINING DATA;

EID: 84862281776     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (36)

References (19)
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    • Exploiting task relatedness for multiple task learning
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    • Ben-David, S.1    Schuller, R.2
  • 5
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    • Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains
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    • (1994) IEEE Trans. on Speech and Audio Processing , vol.2
    • Gauvain, J.L.1    Lee, C.H.2
  • 6
    • 0029288633 scopus 로고
    • Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
    • C. Leggetter and P. Woodland, "Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models," Computer, Speech and Language, vol. 9, 1995.
    • (1995) Computer, Speech and Language , vol.9
    • Leggetter, C.1    Woodland, P.2
  • 8
    • 14344266561 scopus 로고    scopus 로고
    • Improving svm accuracy by training on auxiliary data sources
    • P.Wu and T. G. Dietterich, "Improving svm accuracy by training on auxiliary data sources," in ICML, 2004.
    • (2004) ICML
    • Wu, P.1    Dietterich, T.G.2
  • 9
    • 33947635130 scopus 로고    scopus 로고
    • Regularized adaptation of discriminative classifiers
    • X. Li and J. Bilmes, "Regularized adaptation of discriminative classifiers," in ICASSP, 2006.
    • (2006) ICASSP
    • Li, X.1    Bilmes, J.2
  • 11
    • 0031187873 scopus 로고    scopus 로고
    • A bayesian/information theoretic model of learning to learn via multiple task sampling
    • J. Baxter, "A bayesian/information theoretic model of learning to learn via multiple task sampling," Machine Learning, 1997.
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    • Baxter, J.1
  • 13
    • 0003243224 scopus 로고    scopus 로고
    • Probabilistic outputs for support vector machines and comparison to regularized likelihood methods
    • A.J. Smola et. al., Ed.
    • J. Platt, "Probabilistic outputs for support vector machines and comparison to regularized likelihood methods," in Advances in Large Margin Classifiers, A.J. Smola et. al., Ed., 2000, pp. 61-74.
    • (2000) Advances in Large Margin Classifiers , pp. 61-74
    • Platt, J.1
  • 19
    • 5044231640 scopus 로고    scopus 로고
    • Learning methods for generic object recognition with invariance to pose and lighting
    • Y. LeCun, F. J. Huang, and L. Bottou, "Learning methods for generic object recognition with invariance to pose and lighting," in CVPR, 2004.
    • (2004) CVPR
    • Lecun, Y.1    Huang, F.J.2    Bottou, L.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.