SVM maximizing margin in the input space

ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age

Volumn 2, Issue , 2002, Pages 1069-1073

  Akaho, S ^a  

^a NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; INFORMATION SCIENCE; ITERATIVE METHODS; QUADRATIC PROGRAMMING; VECTOR SPACES;

FEATURE SPACE; INPUT SPACE; QUADRATIC PROGRAMMING PROBLEMS; SUPPORT VECTOR;

SUPPORT VECTOR MACHINES;

EID: 84964526644     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICONIP.2002.1198224     Document Type: Conference Paper

References (6)

1. S. Akaho, Curve fitting that minimizes the mean square of perpendicular distances from sample points, SPIE Vision Geometry II (Selected SPIE Papers on CD-ROM 8, 1999), 237-244 (1993)
2. S. Akaho, SVM maximizing margin in the input space (2002) in preparation http://www.neurosci.aist.go.jp/~akaho/papers.html
3. C. Cortes and V.N. Vapnik, Support vector machines, Machine Learning, 20, pp. 273-297 (1995)
4. K.R. Müller, S. Mika, G. Rätch, K. Tsuda, B.Schölkopf, An Introduction to Kernel-Based Learning Algorithms, IEEE Trans. on Neural Networks, 12, pp. 181-201 (2001)
5. V.N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag (1995)
6. V.N. Vapnik, Statistical Learning Theory, John Wiley & Sons (1998)