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Volumn , Issue , 2009, Pages 562-568

Semi-supervised multi-task learning with task regularizations

Author keywords

[No Author keywords available]

Indexed keywords

GRADIENT DESCENT; LABELED DATA; LEARNING PROBLEM; MULTITASK LEARNING; REAL WORLD DATA; RESEARCH EFFORTS; SEMI-SUPERVISED; UNLABELED DATA;

EID: 77951153521     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2009.66     Document Type: Conference Paper
Times cited : (25)

References (23)
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  • 2
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    • A framework for learning predictive structures from multiple tasks and unlabeled data
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    • A hierarchical bayes model of primary and secondary demand
    • N. Arora, G. M. Allenby, and J. Ginter. A hierarchical bayes model of primary and secondary demand. Marketing Science, 17(1):29-44, 1998.
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    • Arora, N.1    Allenby, G.M.2    Ginter, J.3
  • 6
    • 0346238931 scopus 로고    scopus 로고
    • Task clustering and gating for bayesian multitask learning
    • B. Bakker and T. Heskes. Task clustering and gating for bayesian multitask learning. Journal of Machine Learning Research, 4:83-89, 2003.
    • (2003) Journal of Machine Learning Research , vol.4 , pp. 83-89
    • Bakker, B.1    Heskes, T.2
  • 7
    • 33750729556 scopus 로고    scopus 로고
    • Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
    • M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 2399-2434
    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 8
    • 33749252873 scopus 로고    scopus 로고
    • O. Chapelle, B. Schölkopf, and A. Zien, editors. MIT Press, Cambridge, MA
    • O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi- Supervised Learning. MIT Press, Cambridge, MA, 2006.
    • (2006) Semi- Supervised Learning
  • 14
    • 0030092417 scopus 로고    scopus 로고
    • Lapack-style algorithms and software for solving the generalized sylvester equation and estimating the separation between regular matrix pairs
    • B. Ka°gström and P. Poromaa. Lapack-style algorithms and software for solving the generalized sylvester equation and estimating the separation between regular matrix pairs. ACM Trans. Math. Software, 22:78-103, 1996.
    • (1996) ACM Trans. Math. Software , vol.22 , pp. 78-103
    • Kagström, B.1    Poromaa, P.2
  • 15
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    • Multi-task learning via conic programming
    • T. Kato, H. Kashima, M. Sugiyama, and K. Asai. Multi-task learning via conic programming. In Advances in NIPS 20, pages 737-744, 2008.
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    • Semi-supervised multitask learning
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.