SVC

Support Vector Classifier

    [W,J] = SVC(A,KERNEL,C)
    [W,J] = SVC(A,TYPE,PAR,C)
     W = A*SVC([],KERNEL,C)
     W = A*SVC([],TYPE,PAR,C)

Input
A	Dataset
KERNEL	Untrained mapping to compute kernel by A*(A*KERNEL) during  training, or B*(A*KERNEL) during testing with dataset B. -  String to compute kernel matrices by FEVAL(KERNEL,B,A) Default: linear kernel (PROXM([],'p',1));
TYPE	Kernel type (see PROXM)
PAR	Kernel parameter (see PROXM)
C	Regularization parameter (optional; default: 1)

Output
W	Mapping: Support Vector Classifier
J	Object indices of support objects

Description

Optimizes a support vector classifier for the dataset A by quadratic  programming. The non-linearity is determined by the kernel.  If KERNEL = 0 it is assumed that A is already the kernelmatrix (square).  In this case also a kernel matrix B should be supplied at evaluation by  B*W or MAP(B,W).

There are several ways to define KERNEL, e.g. PROXM([],'r',1) for a  radial basis kernel or by USERKERNEL for a user defined kernel.

If C is NaN this regularisation parameter is optimised by REGOPTC.

See for more possibilties SVCINFO

See also

mappings, datasets, proxm, userkernel, nusvc, rbsvc, regoptc,