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Bo L, Wang L, Jiao L (2006) Feature scaling for kernel fisher discriminant analysis using leave-one-out cross validation. Neural Comput 18:961-78 [PubMed]

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Aronszajn N (1950) Theory of reproducing kernels Transactions Of The American Mathematical Society 68:337-404
Bengio Y (2000) Gradient-based optimization of hyperparameters. Neural Comput 12:1889-900 [PubMed]
Blake CL, Merz CJ (1998) UCI Repository of Machine Learning Databases
Cawley GC, Talbot NLC (2003) Efficient leave-one-out cross validation of kernel Fisher discriminant classifiers Pattern Recognition 36:2585-2592
Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines Machine Learning 46:131-159
Figueiredo MAT (2003) Adaptive sparseness for supervised learning IEEE Transactions On Pattern Analysis And Machine Intelligence 25:1150-1159
Fisher RA (1936) The use of multiple measurements in taxonomic problems Annual Of Eugenics 7:179-188
Fukunaga K (1990) Introduction to statistical pattern recognition (2nd ed)
Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines IEEE Transactions On Neural Networks 13:415-425
Krishnapuram B, Hartemink AJ, Carin L, Figueiredo MA (2004) A Bayesian approach to joint feature selection and classifier design. IEEE Trans Pattern Anal Mach Intell 26:1105-11 [Journal] [PubMed]
Lanckriet GRG, Cristianini N, Bartlett P, Ghaoui LE, Jordan MI (2004) Learning the kernel matrix with semidefinite programming Journal Of Machine Learning Research 5:27-72
Lowe D (1995) Similarity metric learning for a variable-kernel classifier Neural Comput 7:72-85
Luntz A, Brailovsky V (1969) On estimation of characters obtained in statistical procedure of recognition Techicheskaya Kibernetica
Mika S (2002) Kernel fisher discriminants Unpublished doctoral dissertation
Mika S, Ratsch G, Weston J, Scholkopf B, Muller KR (1999) Fisher discriminant analysis with kernels Neural networks for signal processing IX, Hu YH:Larson J:Wilson E:Douglas S, ed. pp.41
Ong CS, Smola AJ (2003) Machine learning with hyperkernels Proceedings of the Twentieth International Conference on Machine Learning :568-575
Ratsch G (2001) Robust boosting via convex optimization Unpublished doctoral dissertation
Rifkin R, Klautau A (2004) In defense of one-vs-all classification Journal Of Machine Learning Research 5:101-141
Scholkopf B, Smola AJ (2001) Learning with kernels: Support vector machines, regularization, optimization, and beyond
Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers Neural Processing Letters 9:293-300
Tipping M (2001) Sparse Bayesian learning and the relevance vector machine J Mach Learn Res 1:211-214
Van Gestel T, Suykens JA, Lanckriet G, Lambrechts A, De Moor B, Vandewalle J (2002) Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis. Neural Comput 14:1115-47 [Journal] [PubMed]
Vapnik V (1995) The Nature of Statistical Learning Theory
Vapnik V (1998) Statistical Learning Theory
Vapnik V, Chapelle O (2000) Bounds on error expectation for support vector machines. Neural Comput 12:2013-36 [PubMed]
Weston J, Mukherjee M, Chapelle O, Pontil M, Poggio T, Vapnik V (2001) Feature selection for SVMs Advances in neural information processing systems, Leen TK:Dietterich TG:Tresp V, ed. pp.668
Williams CKI, Barber D (1998) Bayesian classification with gaussian processes IEEE Trans Patt Anal Mach Intel 20:1342-1351
Xu J, Zhang X, Li Y (2001) Kernel MSE algorithm: A unified framework for KFD, LS-SVM and KRR Proceedings of the International Joint Conference on Neural Networks :1486-1491
(28 refs)