Kernel Statistics Toolbox
(Primitive version, on-going project)
KGaussian: for building Gaussian
SVMs: smooth SVM solved in the primal; Lagrangian
SVM solved in the dual.
KPCA: kernel principal component analysis for
KSIR: kernel sliced inverse regression for
KFDA: kernel Fisher linear discriminant
analysis for multiclass classification.
KCCA: kernel canonical correlation analysis.
SVRs: regularized LS SVR; smooth epsilon-insensitive SVR.
kernel cluster analysis
hibiscus-plus: a collection of UD-based
model selection programs for SVM and SVR. It contains 3 main codes: hibiscus4LSSVR
(for regularized least square SVR), hibiscus4mSSVR (for multi-response
epsilon-SVR. It now only supports epsilon-SSVR. Support for LIBSVM for
regression is in development.) and hibiscus4SVM (for SVM, it supports SSVM,
LIBSVM and Lagrangian SVM).
S.Y. Huang, K.Y. Lee and H.S. Lu.
Lecture Notes (draft version) on Statistical
and Machine Learning
Last modified: 2007-05-23
This toolbox is maintained on http://184.108.40.206/kern_stat_toolbox/kernstat.html