Algorithmic luckiness

Advances in Neural Information Processing Systems

Volumn , Issue , 2002, Pages

  Herbrich, Ralf ^a   Williamson, Robert C ^b  

^a MICROSOFT RESEARCH   (United Kingdom)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEXITY MEASURES; FEATURE SPACE; HYPOTHESIS SPACE; LINEAR CLASSIFIERS; MAXIMUM MARGIN; STATISTICAL LEARNING THEORY; TRAINING SAMPLE;

LEARNING ALGORITHMS;

EID: 0041995176     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

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2. O. Bousquet and A. Elisseeff. Algorithmic stability and generalization performance. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, pages 196-202. MIT Press, 2001.
3. N. Cristianini, A. Elisseeff, and J. Shawe-Taylor. On optimizing kernel alignment. Technical Report NC2-TR-2001-087, NeuroCOLT, http://www.neurocolt.com 2001.
4. R. Herbrich and T. Graepel. A PAC-Bayesian margin bound for linear classifiers: Why SVMs work. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, pages 224-230, Cambridge, MA, 2001. MIT Press.
5. R. Herbrich and R. C. Williamson. Algorithmic luckiness. Technical report, Microsoft Research, 2002.
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8. J. Shawe-Taylor, P. L. Bartlett, R. C. Williamson, and M. Anthony. Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory, 44(5):1926-1940, 1998.
9. V. Vapnik. Statistical Learning Theory. John Wiley and Sons, New York, 1998.
10. V. N. Vapnik and A. Y. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16(2):264-281, 1971.