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

See more from authors: Bo L · Wang L · Jiao L

References and models cited by this paper

Aronszajn N. (1950). Theory of reproducing kernels Transactions Of The American Mathematical Society. 68

Bengio Y. (2000). Gradient-based optimization of hyperparameters. Neural computation. 12 [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

Figueiredo MAT. (2003). Adaptive sparseness for supervised learning IEEE Transactions On Pattern Analysis And Machine Intelligence. 25

Fisher RA. (1936). The use of multiple measurements in taxonomic problems Annual Of Eugenics. 7

Fukunaga K. (1990). Introduction to statistical pattern recognition (2nd ed).

Jordan MI, Bartlett P, Cristianini N, Ghaoui LE, Lanckriet GRG. (2004). Learning the kernel matrix with semidefinite programming Journal Of Machine Learning Research. 5

Krishnapuram B, Hartemink AJ, Carin L, Figueiredo MA. (2004). A Bayesian approach to joint feature selection and classifier design. IEEE transactions on pattern analysis and machine intelligence. 26 [PubMed]

Li Y, Xu J, Zhang X. (2001). Kernel MSE algorithm: A unified framework for KFD, LS-SVM and KRR Proceedings of the International Joint Conference on Neural Networks .

Lin CJ, Hsu CW. (2002). A comparison of methods for multiclass support vector machines IEEE Transactions On Neural Networks. 13

Lowe D. (1995). Similarity metric learning for a variable-kernel classifier Neural Comput. 7

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 Kernel fisher discriminants Unpublished doctoral dissertation.

Ong CS, Smola AJ. (2003). Machine learning with hyperkernels Proceedings of the Twentieth International Conference on Machine Learning.

Poggio T et al. (2001). Feature selection for SVMs Advances in neural information processing systems. 13

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 Robust boosting via convex optimization Unpublished doctoral dissertation.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

Scholkopf B, Muller KR, Mika S, Ratsch G, Weston J. (1999). Fisher discriminant analysis with kernels Neural networks for signal processing IX.

Scholkopf B, Smola AJ. (2001). Learning with kernels: Support vector machines, regularization, optimization, and beyond.

Tipping M. (2001). Sparse Bayesian learning and the relevance vector machine J Mach Learn Res. 1

Van Gestel T et al. (2002). Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis. Neural computation. 14 [PubMed]

Vandewalle J, Suykens JAK. (1999). Least squares support vector machine classifiers Neural Processing Letters. 9

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 computation. 12 [PubMed]

Vapnik V, Mukherjee S, Chapelle O, Bousquet O. (2002). Choosing multiple parameters for support vector machines Machine Learning. 46

Williams CKI, Barber D. (1998). Bayesian classification with gaussian processes IEEE Trans Patt Anal Mach Intel. 20

References and models that cite this paper
This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.