Is the maximal margin hyperplane special in a feature space?

HP Laboratories Technical Report

Volumn , Issue 89, 2001, Pages

  Zhang, Bin ^a  

^a HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

Machine learning; Supervised learning; Support Vector Machines (SVM)

Indexed keywords

GENERALIZATION; INPUT SPACES; KERNEL METHOD; SUPPORT VECTOR MACHINES (SVM);

ALGORITHMS; DATA REDUCTION; LEARNING SYSTEMS; MAPPING; RISK ASSESSMENT; STATISTICAL METHODS;

VECTORS;

EID: 84862450878     PISSN: None     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (8)

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2. th International Workshop on Computational Learning Theory, p144-152.
3. Cortes, C. & Vapnik, V. (1995), Support-Vector Networks, Machine Learning 20, p273-297.
4. Cristianini, N. & Shawe-Taylor, J., An Introduction to Support Vector Machines and Other Kernel Based Learning Methods, Cambridge University Press, 2000.
5. Duda, R. & Hart, P. (1972), Pattern Classification and Scene Analysis, John Wiley & Sons.
6. Osuna, E., Freund, R. & Girosi, F. (1997), Training Support Vector Machines: An application to face detection, In Proc. Computer Vision and Pattern Recognition '97, p130-136.
7. Scholkopf, B., Burges, C. & Smola, A. (1998), Advances in Kernel Methods - Support Vector Learning, MIT Press.
8. Vapnik, V. (1998), Statistical Learning Theory, John Wiley & Sons.