How good is a kernel when used as a similarity measure?

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4539 LNAI, Issue , 2007, Pages 323-335

  Srebro, Nathan ^a,b  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

^b IBM HAIFA RESEARCH LAB   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

BRANCH AND BOUND METHOD; ERROR ANALYSIS; FUNCTIONS; NUMERICAL METHODS; PROBLEM SOLVING;

ERROR-RATES; LEARNING GUARANTEES; SIMILARITY FUNCTIONS;

LEARNING SYSTEMS;

EID: 38049018945     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72927-3_24     Document Type: Conference Paper

References (7)

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3. Viswanathan, S., Smola, A.J.: Fast kernels for string and tree matching. In: Advances in Neural Information Processing Systems 15. MIT Press (2003)
4. Smola, A.J., Schölkopf, B.: Learning with Kernels. MIT Press (2002)
5. McAllester, D.: Simplified PAC-bayesian margin bounds. In: Proceedings of the 16th Annual Conference on Learning Theory. (2003)
6. Bartlett, P.L., Mendelson, S.: Rademacher and gaussian complexities: risk bounds and structural results. J. Mach. Learn. Res. 3 (2003) 463-482
7. Hettich, R., Kortanek, K.O.: Semi-infinite programming: theory, methods, and applications. SIAM Rev. 35 (1993) 380-429