Stats Support Vector Classifier (Matlab Stats Toolbox)
W = STATSVC(A,KERNEL,C,OPTTYPE)
This is the PRTools interface to the support vector classifier SVMTRAIN in Matlab's Stats toolbox. Like PRTools SVC it is a two-class discriminant that also can be used for multi-class problems by an internal call to MCLASSC. SVMTRAIN uses the Sequential Minimal Optimization (SMO) method and thereby implements the L1 soft-margin SVM classifier. See SVMTRAIN for more details.
Non-linear kernels have to be supplied by kernel procedures like PROXM. It is assumed that V = A*KERNEL generates the trained kernel and B*V the kernel matrix with size(B,1) rows and size(A,1) columns. Forthe radial basis kernel use PKSTATSSVC and RBSTATSSVC
Like for all other PRTools classifiers, a new dataset B can be classified by D = B*W. The classifier performance can be measured by D*TESTC and the resulting labels by D*LABELD. D is a dataset with for every class a column. The values can be considered as class confidences or classifier condaitional posteriors.
STATSSVM is basically a two-class classifier. Multiclass problems are internally solved using MCLASSC resulting in a base classifier per class. The final result may be improved significantly by using a non-linear trained combiner, e.g. by calling W = A*(STATSSVM*QDC(,,1e-6);
Alternative SVM classifiers in PRTools are based on SVC and LIBSVC.