Abstract
The aim of the present paper is twofold. Firstly an introduction to the ideas of Support Vector regression is given. then a new and simple algorithm, suggested by the work of Campbell y Cristianini in [16], is proposed which solves the corresponding quadratic programming problem in an easy fashion. The algorithm is illustrated by example and compared with classical regression.
References
Vapnik, V. (1995) The Nature of Statistical Learning Theory. Springer-Verlag, NewYork.
Vapnik, V. (1998) Statistical Learning Theory. John Wiley and Sons, New York.
Ganapathiraju, A.; Hamaker, J.; Picone, J. (1998) “Support vector machines for speech recognition”, Proc. ICSLP, Australia.
Weston, J.; Gammerman, A.; Stitson, M.; Vapnik, V.; Vovk, V.; Watkins. (1998) “Density estimation using support vector machines”, Technical Report CSD-TR-97-23.
Müller, K.; Smola, A.; Rätsch, G.; Schölkopf, B.; Kohlmorgan, J.; Vapnik, V. (1997) “Predicting time series with support vector machines”, Proc. of the ICANN Conference.
Burges, C. (1998) “A Tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery, 2(2).
Vapnik, V.; Golowich, S.; y Smola, A. (1997) “Support vector method for function approximation, regression estimation, and signal processing”, Advances in Neural In-formation Processing Systems, Vol. 9, MIT Press, Cambridge Mass.: 281–287.
Smola, A.; Schölkopf, B. (1998) “A tutorial on support vector regression”, Neuro-COLT2 Technical Report Series, NC2-TR-030.
Gunn, S. (1998) “Support vector machines for classification and regression”, ISIS Technical Report, University of Southampton.
Stitson, M.; Weston, J.; Gammerman, A.; Vovk V.; Vapnik, V. (1996) “Theory of support vector machines”, Tech. Report CSD-TR-96-17, RHUL.
Girosi, F. (1998) “An equivalence between sparse approximation and support vector machines”, Neural Computation, 10(6): 1455–1480.
Chin, K. (1998) “Support Vector Machines applied to Speech Pattern Classification”. M.Phil. Thesis in Computer Speech and Language Processing, Cambridge University, Engineering Department.
Vanderbei, R.J. (1997) “LOQO user’s manual 3.10”, Technical Report SOR-97-08, Statistics and Operations Research, Princeton University.
Osuna, E.; Freund, R.; Girosi, F. (1997) “An improved training algorithm for support vector machines”, Neural Networks for Signal Processing VII - Proc. of the 1997 IEEE Workshop, J. Principe, L. Gile, N. Morgan, y E. Wilson (Eds.): 276–285.
Platt, J. (1998) “Sequential minimal optimization: A fast algorithm for training support vector machines”, Advances in Kernel Methods- Support Vector Learning, B. Schölkopf, C. Burges, A. Smola (Eds.) MIT Press, Cambridge Mass.
Campbell, C.; Cristianini, N. (1999) “Simple learning algorithms for training support vector machines”, Department of Engineering Mathematics, University of Bristol.