Graphics Reference
In-Depth Information
Aronszajn, N. ( ). heory of reproducing kernels. Transactions of the American
Mathematical Society, : - .
Bach,F. and Jordan, M.I. ( ).Kernel independent component analysis. Journal of
Machine Learning Research, : - .
Ben-Hur, A., Horn, D., Siegelmann, H.T. and Vapnik, V. ( ). Support vector clus-
tering. Journal of Machine Learning Research, : - .
Berlinet, A. and homas-Agnan, C. ( ). Reproducing Kernel Hilbert Spaces in
Probability and Statistics.Kluwer,Boston,MA.
Boser, B.E., Guyon, I.M. and Vapnik, V.N. ( ). A training algorithm for optimal
margin classifiers. In:Valient, L.andWarmuth, M.(eds) Proceedings of the th An-
nual ACM Workshop on Computational Learning heory. ACM Press, Pittsburgh,
PA, : - .
Hardoon, D.R.,Szedmak, S. and Shawe-Taylor, J.( ).Canonical correlation anal-
ysis: An overview with application to learning methods. Neural Computation,
( ): - .
Hein,M.andBousquet,O.( ).Kernels,associatedstructuresandgeneralizations.
Technical report, Max Planck Institute for Biological Cybernetics, Germany. http:
//www.kyb.tuebingen.mpg.de/techreports.html.
Hotelling, H. ( ). Relations between two sets of variates. Biometrika, : - .
Lee, Y.J., Hsieh, W.F. and Huang, C.M. ( ). є-SSVR: A smooth support vector
machine for є-insensitive regression. IEEE Transactions on Knowledge and Data
Engineering, ( ): - .
Lee, Y.J. and Huang, S.Y. ( ). Reduced support vector machines: a statistical the-
ory. IEEE Transactions on Neural Networks, : - .
Lee, Y.J. and Mangasarian, O.L. ( a). RSVM: Reduced support vector machines.
In:Kumar,V.andGrossman,R.(eds)Proceedings of the First SIAM International
Conference on Data Mining. SIAM, Philadephia, PA.
Lee, Y.J. and Mangasarian, O.L. ( b). SSVM: A smooth support vector machine
for classification. Computational Optimization and Applications, ( ): - .
MacQueen, J.B. ( ). Some methods for classification and analysis of multivariate
observations. In:LeCam,L.and Neyman,J.(eds) Proceedings of th Berkeley Sym-
posium on Mathematical Statistics and Probability. University of California Press,
Berkeley, CA, : - .
Mardia,K.V.,Kent,J.T.andBibby,J.M.( ).Multivariate Analysis. Probability and
Mathematical Statistics: A Series of Monographs and Textbooks. Academic, New
York.
Schölkopf, B., Burges, C. and Smola, A. ( ). Kernel principal component analy-
sis. Advances in Kernel Methods - Support Vector Learning, - . MIT Press,
Cambridge, MA.
Schölkopf, B., Smola, A. and Müller, K. ( ). Nonlinear component analysis as a
kernel eigenvalue problem. Neural Computation, ( ): - .
Vapnik, V.N. ( ). he Nature of Statistical Learning heory.Springer-Verlag,New
York.
Search WWH ::




Custom Search