Maximal margin classification for metric spaces

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2777, Issue , 2003, Pages 72-86

  Hein, Matthias ^a   Bousquet, Olivier ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); HEURISTIC METHODS; ISOMERS; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; MATRIX ALGEBRA; PROBLEM SOLVING; BANACH SPACES; HILBERT SPACES; SET THEORY; SUPPORT VECTOR MACHINES; TOPOLOGY;

CONDITIONALLY POSITIVE DEFINITE (CPD); HILBERT SPACES; RADEMACHER AVERAGES; SUPPORT VECTOR MACHINES (SVM); CLASSIFICATION ALGORITHM; FUNCTION CLASS; MAXIMAL MARGIN; MAXIMAL MARGIN ALGORITHMS; METRIC SPACES; POSITIVE DEFINITE; SQUARED DISTANCES;

SPACE APPLICATIONS; VECTOR SPACES;

EID: 9444267627     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-45167-9_7     Document Type: Conference Paper

References (12)

1. P. L. Bartlett, S. Mendelson, Rademacher and Gaussian Complexities: Risk Bounds and Structural Results, JLMR, 3, 463-482, (2002).
2. K. P. Bennett,E. J. Bredensteiner, Duality and Geometry in SVM classifiers, Proceedings of the Seventeenth International Conference on Machine Learning, 57-64, (2000).
3. C. Berg, J.P.R. Cristensen, P. Ressel, Harmonic Analysis on Semigroups, Springer Verlag, New York, (1984).
4. F. Cucker, S. Smale, On the Mathematical Foundations of Learning, Bull. Amer. Math. Soc., 39, 1-49, (2002).
5. R. M. Dudley, Universal Donsker Classes and Metric Entropy, Ann. Prob.,15,1306-1326, (1987).
6. T. Graepel, R. Herbrich, B. Schölkopf, A. Smola, P. Bartlett, K.R. Müller, K. Obermayer and R. Williamson, Classification on proximity data with LP-machines, International Conference on Artificial Neural Networks, 304-309, (1999).
7. E. Pekalska, P. Paclik, R.P.W. Duin, A Generalized Kernel Approach to Dissimilarity-based Classification, Journal of Machine Learning Research, 2, 175-211, (2001).
8. W. Rudin, Functional Analysis, McGraw Hill, (1991).
9. I. J. Schoenberg, Metric Spaces and Positive Definite Functions, TAMS, 44, 522-536, (1938).
10. B. Schölkopf, The Kernel Trick for Distances, Neural Information Processing Systems (NIPS), 13, (2000).
11. B. Schölkopf, A. J. Smola Learning with Kernels, MIT Press, MA, Cambridge, (2002).
12. D. Zhou, B. Xiao, H. Zhou, R. Dai, Global Geometry of SVM Classifiers, Technical Report 30-5-02, AI Lab, Institute of Automation, Chinese Academy of Sciences, (2002).