Maximum margin coresets for active and noise tolerant learning

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 836-841

  Har Peled, Sariel ^a   Roth, Dan ^a   Zimak, Dav ^b  

^a University of Illinois at Urbana Champaign   (United States)

^b YAHOO INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; AGNOSTIC LEARNING; DIRECT ALGORITHMS; LARGE-MARGIN LEARNING; MAXIMUM MARGIN; MULTICLASS CLASSIFICATION PROBLEMS; NOISE-TOLERANT LEARNING; POLYNOMIAL-TIME;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; POLYNOMIAL APPROXIMATION;

INPUT OUTPUT PROGRAMS;

EID: 84880867839     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. D. Angluin. Queries and concept learning. Machine Learning, 2(4):319-342, 1987.
2. M. Badoiu and K.L. Clarkson. Smaller core-sets for balls. In SODA, pages 801-802, 2003.
3. C. Campbell, N. Cristianini, and A.J. Smola. Query learning with large margin classifiers. In Proceedings of the Seventeeth ICML, pages 111-118, 2000.
4. S. Dasgupta. Analysis of a greedy active learning strategy. In NIPS, volume 17, pages 337-344. MIT Press, 2005.
5. Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 55(1):119-139, 1997. (Pubitemid 127433398)
6. C. Gentile. A new approximate maximal margin classification algorithm. JMLR, 2:213-242, 2001.
7. T. Joachims. Training linear svms in linear time. In Proceedings of the Twelfth ACM SIGKDD, New York, 2006.
8. M. Kearns, M. Li, L. Pitt, and L. Valiant. Recent results on boolean concept learning. In Proceedings of the Fourth International Workshop on Machine Learning, pages 337-352, 1987.
9. A. Kowalczyk. Maximal margin perceptron. In Advances in Large Margin Classifiers. MIT Press, Cambridge MA, 2000.
10. M. Kearns and R. Schapire. Efficient distribution-free learning of probabilistic concepts. In Computational Learning Theory and Natural Learning Systems, Constraints and Prospect, volume 1. The MIT Press, 1994.
11. Y. Li and P.M. Long. The relaxed online maximum margin algorithm. Machine Learning, 46(1-3):361-387, 2002. (Pubitemid 34129976)
12. E. Osuna, R. Freund, and F. Girosi. Improved training algorithm for support vector machines. NNSP, 1997.
13. J. Platt. Sequential minimal optimization: A fast algorithm for training support vector machines. Technical Report 98-14, Microsoft Research, April 1998.
14. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In Proceedings of the twenty-first ICML, page 104. ACM Press, 2004.
15. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. JMLR, 2:45-66, 2002.
16. I.W. Tsang, J.T. Kwok, and P. Cheung. Core vector machines: Fast svm training on very large data sets. JMLR, 6:363-392, 2005.
17. V.N. Vapnik. Estimation of Dependences Based on Empirical Data. Springer Series in Statistics. Springer, New York, 1982.
18. V.N. Vapnik. The nature of statistical learning theory. Springer Verlag, Heidelberg, DE, 1995.