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Volumn , Issue , 1999, Pages 225-231

Linear hinge loss and average margin

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ALGORITHM; LOSS BOUNDS; LOSS FUNCTIONS; PERCEPTRON; WINNOW ALGORITHM;

EID: 84898954071     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (108)

References (11)
  • 2
    • 49949144765 scopus 로고
    • The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
    • L.M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Physics, 7:200-217, 1967.
    • (1967) USSR Computational Mathematics and Physics , vol.7 , pp. 200-217
    • Bregman, L.M.1
  • 3
    • 0031624445 scopus 로고    scopus 로고
    • Large margin classification using the perceptron algorithm
    • ACM
    • Y. Freund and R. Schapire. Large margin classification using the perceptron algorithm. In 11th COLT, pp. 209-217, ACM, 1998.
    • (1998) 11th COLT , pp. 209-217
    • Freund, Y.1    Schapire, R.2
  • 4
    • 0030661191 scopus 로고    scopus 로고
    • General convergence results for linear discriminant updates
    • ACM
    • A. J. Grove, N. Littlestone, and D. Schuurmans. General convergence results for linear discriminant updates. In 10th COLT, pp. 171-183. ACM, 1997.
    • (1997) 10th COLT , pp. 171-183
    • Grove, A.J.1    Littlestone, N.2    Schuurmans, D.3
  • 5
    • 0346641007 scopus 로고
    • Worst-case loss bounds for sigmoided linear neurons
    • MIT Press, 1995
    • D. P. Helmbold, J. Kivinen, and M. K. Warmuth. Worst-case loss bounds for sigmoided linear neurons. In NIPS 1995, pp. 309-315. MIT Press, 1995.
    • (1995) NIPS , pp. 309-315
    • Helmbold, D.P.1    Kivinen, J.2    Warmuth, M.K.3
  • 6
    • 0008815681 scopus 로고    scopus 로고
    • Additive versus exponentiated gradient updates for linear prediction
    • J. Kivinen and M. K. Warmuth. Additive versus exponentiated gradient updates for linear prediction. Inform, and Comput., 132(1): 1-64, 1997.
    • (1997) Inform, and Comput. , vol.132 , Issue.1 , pp. 1-64
    • Kivinen, J.1    Warmuth, M.K.2
  • 7
    • 0008969040 scopus 로고    scopus 로고
    • Relative loss bounds for multidimensional regression problems
    • MIT Press
    • J. Kivinen and M. K. Warmuth. Relative loss bounds for multidimensional regression problems. In NIPS 10, pp. 287-293. MIT Press, 1998.
    • (1998) NIPS , vol.10 , pp. 287-293
    • Kivinen, J.1    Warmuth, M.K.2
  • 8
    • 0031375503 scopus 로고    scopus 로고
    • The perceptron algorithm vs. Winnow: Linear vs. Logarithmic mistake bounds when few input variables are relevant
    • J. Kivinen, M. K. Warmuth, and P. Auer. The perceptron algorithm vs. winnow: linear vs. logarithmic mistake bounds when few input variables are relevant. Artificial Intelligence, 97:325-343, 1997.
    • (1997) Artificial Intelligence , vol.97 , pp. 325-343
    • Kivinen, J.1    Warmuth, M.K.2    Auer, P.3
  • 9
    • 34250091945 scopus 로고
    • Learning when irrelevant attributes abound: A new linear-threshold algorithm
    • N. Littlestone. Learning when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2:285-318, 1988.
    • (1988) Machine Learning , vol.2 , pp. 285-318
    • Littlestone, N.1
  • 11
    • 0000511449 scopus 로고
    • Redundant noisy attributes, attribute errors, and linear threshold learning using winnow
    • Morgan Kaufmann
    • N. Littlestone. Redundant noisy attributes, attribute errors, and linear threshold learning using Winnow. In 4th COLT, pp. 147-156, Morgan Kaufmann, 1991.
    • (1991) 4th COLT , pp. 147-156
    • Littlestone, N.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.