Support vector machine learning algorithm and transduction

Computational Statistics

Volumn 15, Issue 1, 2000, Pages 31-39

  Gammermann, A ^a  

^a UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Kernels; Randomness; Support vector machine; Transduction

Indexed keywords

EID: 0039606361     PISSN: 09434062     EISSN: None     Source Type: Journal    
DOI: 10.1007/s001800050034     Document Type: Article

References (7)

1. Vapnik, V. N. (1998), Statistical Learning Theory. Wiley, New York.
2. Saunders, C., Gammerman, A.& Vovk, V. (1998), Ridge Regression Learning Algorithm in Dual Variables. Machine Learning, Proceedings of the Fifteenth International Conference (ICML'98), pp.515-521, edited by Jude Shavlik, Morgan Kaufmann Publishers, San Francisco.
3. Gammerman, A. J. (1996), Machine Learning: progress and prospects. ISBN 0 0900145 93 5, University of London, 1996.
4. Gammerman, A., Vovk, V, & Vapnik, V. (1998), Learning by Transduction. Uncertainty in Artificial Intelligence, Proceedings of the Fourteenth Conference, pp.148-155, edited by G.F. Cooper and S. Moral, Morgan Kaufmann Publishers, San Francisco.
5. Vovk, V, Gammerman, A.& Saunders, C. (1999), Machine Learning Applications of Algorithmic Randomness. Machine Learning, Proceedings of the Sixteenth International Conference (ICML '99), pp.444-453, edited by I. Bratko and S. Dzeroski, Morgan Kaufmann Publishers, San Francisco.
6. Wahba, G. (1978), Improper priors, spline smoothing and the problem of guarding against model errors in regression. J.Roy.Stat.Soc. Ser.B, 40:364-372, 1978.
7. Li, M. and Vitanyi, P. (1997), An introduction to Kolmogorov complexity and its applications. Springer, New York, 2nd edition, 1997.