메뉴 건너뛰기




Volumn 11, Issue , 2010, Pages 2901-2934

Linear algorithms for online multitask classification

Author keywords

Mistake bounds; Multitask learning; Perceptron algorithm; Spectral regularization

Indexed keywords

BINARY CLASSIFICATION PROBLEMS; LINEAR ALGORITHMS; MATRIX; MISTAKE BOUNDS; MULTI-TASK KERNELS; MULTITASK LEARNING; ON-LINE SETTING; PERCEPTRON; PERCEPTRON ALGORITHMS; REAL WORLD DATA; REFERENCE VECTORS; REGULARITY CONDITION; SPECTRAL REGULARIZATION; TASK RELATEDNESS;

EID: 78649437219     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (144)

References (38)
  • 2
    • 78049387979 scopus 로고    scopus 로고
    • Matrix regularization techniques for online multitask learning
    • EECS Department, University of California, Berkeley
    • A. Agarwal, A. Rakhlin, and P. Bartlett. Matrix regularization techniques for online multitask learning. Technical Report UCB/EECS-2008-138, EECS Department, University of California, Berkeley, 2008.
    • (2008) Technical Report UCB/EECS-2008-138
    • Agarwal, A.1    Rakhlin, A.2    Bartlett, P.3
  • 3
    • 27844439373 scopus 로고    scopus 로고
    • A framework for learning predictive structures from multiple tasks and unlabeled data
    • R. K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6:1817-1853, 2005.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 1817-1853
    • Ando, R.K.1    Zhang, T.2
  • 6
    • 0037403111 scopus 로고    scopus 로고
    • Mirror descent and nonlinear projected subgradient methods for convex optimization
    • A. Beck and M. Teboulle. Mirror descent and nonlinear projected subgradient methods for convex optimization. Operation Research Letters, 31:167-175, 2003.
    • (2003) Operation Research Letters , vol.31 , pp. 167-175
    • Beck, A.1    Teboulle, M.2
  • 11
    • 78649432945 scopus 로고    scopus 로고
    • ECML/PKDD Discovery Challenge, URL
    • ECML/PKDD Discovery Challenge, 2006. URL: www.ecmlpkdd2006.org/challenge. html.
    • (2006)
  • 13
    • 0033281425 scopus 로고    scopus 로고
    • Large margin classification using the perceptron algorithm
    • Y. Freund and R. E. Schapire. Large margin classification using the perceptron algorithm. Machine Learning, 37(3):277-296, 1999.
    • (1999) Machine Learning , vol.37 , Issue.3 , pp. 277-296
    • Freund, Y.1    Schapire, R.E.2
  • 14
    • 0344875562 scopus 로고    scopus 로고
    • The robustness of the p-norm algorithms
    • C. Gentile. The robustness of the p-norm algorithms. Machine Learning, 53(3):265-299, 2003.
    • (2003) Machine Learning , vol.53 , Issue.3 , pp. 265-299
    • Gentile, C.1
  • 15
    • 0035370643 scopus 로고    scopus 로고
    • General convergence results for linear discriminant updates
    • A. J. Grove, N. Littlestone, and D. Schuurmans. General convergence results for linear discriminant updates. Machine Learning, 43(3):173-210, 2001.
    • (2001) Machine Learning , vol.43 , Issue.3 , pp. 173-210
    • Grove, A.J.1    Littlestone, N.2    Schuurmans, D.3
  • 25
    • 0035575628 scopus 로고    scopus 로고
    • Relative loss bounds for multidimensional regression problems
    • J. Kivinen and M. Warmuth. Relative loss bounds for multidimensional regression problems. Machine Learning, 45:301-329, 2001.
    • (2001) Machine Learning , vol.45 , pp. 301-329
    • Kivinen, J.1    Warmuth, M.2
  • 26
    • 0039119760 scopus 로고
    • The convex analysis of unitarily invariant matrix functions
    • A. S. Lewis. The convex analysis of unitarily invariant matrix functions. Journal of Convex Analsys, 2(1):173-183, 1995.
    • (1995) Journal of Convex Analsys , vol.2 , Issue.1 , pp. 173-183
    • Lewis, A.S.1
  • 32
    • 78649422646 scopus 로고    scopus 로고
    • NIST, URL
    • NIST, 2004. URL: trec.nist.gov/data/reuters/reuters.html.
    • (2004)
  • 35
    • 21844471282 scopus 로고    scopus 로고
    • Matrix exponentiated gradient updates for on-line learning and Bregman projection
    • K. Tsuda, G. Raetsch, and M. K. Warmuth. Matrix exponentiated gradient updates for on-line learning and Bregman projection. Journal of Machine Learning Research, 6:995-1018, 2005.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 995-1018
    • Tsuda, K.1    Raetsch, G.2    Warmuth, M.K.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.