SVC

Trainable classifier: Support Vector Machine

    [W,J] = SVC(A,KERNEL,C)
    [W,J] = A*SVC([],KERNEL,C)
    [W,J] = A*SVC(KERNEL,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))
C	Regularisation parameter (optional; default: 1)

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

Description

Optimises 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 PRMAP(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.

SVC is basically a two-class classifier. Multi-class problems are solved  in a one-against-rest fashion by MCLASSC. The resulting base-classifiers  are combined by the maximum confidence rule. A better, non-linear  combiner might be FISHERCC, e.g. W = A*(SVC*FISHERCC)

See for more possibilties SVCINFO

Example(s)

 a = gendatb;                     % generate banana classes
 [w,J] = a*svc(proxm('p',3));  % compute svm with 3rd order polynomial
 a*w*testc                        % show error on train set
 scatterd(a)                      % show scatterplot
 plotc(w)                         % plot classifier
 hold on; 
 scatterd(a(J,:),'o')             % show support objcts

See also

mappings, datasets, proxm, userkernel, nusvc, rbsvc, libsvc, regoptc, mclassc, fishercc,