Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces

Mika Sirén1, Gunnar Rätsch2, Jason Weston3, Bernhard Schölkopf3, Alexander J. Smola2, K. Müller1,4
1Fraunhofer FIRST, Berlin, Germany
2Australian National University, Canberra, ACT, Australia
3Max Planck Institut für biologische Kybernetik, Tubingen, Germany
4University of Potsdam, Potsdam, Germany

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10.1162/153244302760200704

tipping, 2000, The Relevance Vector Machine, Proc Conf Advances in Neural Information Processing Systems, 12, 652

bishop, 1995, Neural Networks for Pattern Recognition

saunders, 1998, Ridge Regression Learning Algorithm in Dual Variables, Proc 15th Int'l Conf Machine Learning, 515

10.1023/A:1018628609742

10.1109/TPAMI.2002.1033211

sch�lkopf, 2001, A Generalized Representer Theorem, Proc COLT/EuroCOLT, 416

10.1145/130385.130401

vapnik, 1998, Statistical Learning Theory

10.1162/089976600300014980

10.1109/72.914517

duda, 1973, Pattern Classification and Scene Analysis

10.1111/j.1469-1809.1936.tb02137.x

diamantaras, 1996, Principal Component Neural Networks

10.1162/089976698300017467

2002

10.1109/72.788641

graepel, 1999, classification on proximity data with lp-machines, Proceedings of 9th International Conference on Artificial Neural Networks ICANN 99, 1, 304, 10.1049/cp:19991126

r�tsch, 2002, Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces, Machine Learning, 48, 193

blake, 1998, UCI Repository of Machine Learning Databases

mika, 2001, An Improved Training Algorithm for Kernel Fisher Discriminants, Proc AISTATS, 98

simard, 1998, Transformation Invariance in Pattern RecognitionTangent Distance and Tangent Propagation, Neural Networks Tricks of the Trade, 1524, 239, 10.1007/3-540-49430-8_13

10.1016/S0893-6080(98)00032-X

mika, 2001, A Mathematical Programming Approach to the Kernel Fisher Algorithm, Proc Conf Neural Information Processing Systems, 13, 591

platt, 1999, Fast Training of Support Vector Machines Using Sequential Minimal Optimization, Advances in Kernel MethodsSupport Vector Learning, 185

10.1109/NNSP.1999.788121

keerthi, 2002, SMO Algorithm for Least Squares SVM Formulations

sch�lkopf, 2002, Learning with kernels

roth, 2000, Nonlinear Discriminant Analysis Using Kernel Functions, Proc Conf Advances in Neural Information Processing Systems, 12, 568

gestel, 2001, Bayesian Framework for Least Squares Support Vector Machine Classifiers, Gaussian Processs and Kernel Fisher Discriminant Analysis