Cheat-sheetSDSVC Support vector machine (trained by libSVM) P=SDSVC(DATA,options) [P,E]=SDSVC(DATA,options) INPUT DATA SDDATA object OUTPUT P Pipeline object E Structure with grid search errors OPTIONS 'type' kernel type: 'RBF','poly','linear' (default: RBF) 'sigma' RBF sigma (default: select by grid-search) 'degree' Polynomial degree (default: select by grid-search) 'C' cost parameter C (default: select by grid-search) 'noscale' Do not include data scaling 'test' Provide external sddata for evaluating error in parameter search 'tsfrac' If 'test' is not specified, fraction of DATA selected randomly per class for evaluating error criterion (def: 0.25) DESCRIPTION SDSVC trains a support vector machine using libSVM. By default, RBF kernel is used with sigma and C parameters optimized using grid search minimizing mean error. Polynomial and linear SVM is available using 'type' option. For multi-class problems, one-against-all strategy is adopted. By default, for RBF and polynomial kernel, sdsvc scales data (standardization). Scaling may be switched off using 'noscale' option. sdsvc is splitting the DATA into a subset used for training the model and a subset used for error estimatiom/parameter selection (by default 25% od DATA). This fraction may be adjusted by 'tsfrac' option. Alternatively, the user may provide external set for error estimation using 'test' option. Returned support vectors contain index of original object in DATA in 'original' property. EXAMPLES p=sdsvc(b) origSV=b( p{2}.proto.original ) % p{2} because the first step is scaling READ MORE http://perclass.com/doc/guide/classifiers.html#sdsvc REFERENCE Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. http://www.csie.ntu.edu.tw/~cjlin/libsvm
sdsvc is referenced in examples: