NULIBSVC

Trainable classifier: LIBSVM, nu-algorithme

    [W,J,NU] = NULIBSVC(A,KERNEL,NU)
    [W,J,NU] = A*NULIBSVC([],KERNEL,NU)
    [W,J,NU] = A*NULIBSVC(KERNEL,NU)

Input
A	Dataset
KERNEL	Mapping to compute kernel by A*MAP(A,KERNEL) or string to compute kernel by FEVAL(KERNEL,A,A) or cell array with strings and parameters to compute kernel by
FEVAL(KERNEL{1},A,A,KERNEL{2:END})	Default: linear kernel.
NU	nu value, upperbound error.  Default NU is derived from 1-NN error.

Output
W	Mapping: Support Vector Classifier
J	Object idences of support objects. Can be also obtained as W{4}
NU	Actual nu_value used

Description

Optimises a support vector classifier for the dataset A by the libsvm  package, see http://www.csie.ntu.edu.tw/~cjlin/libsvm/. LIBSVC calls the  svmtrain routine of libsvm for training. Classifier execution for a  test dataset B may be done by D = B*W; In D posterior probabilities are  given as computed by svmpredict using the '-b 1' option.

The kernel may be supplied in KERNEL by

• an untrained mapping, e.g. a call to PROXM like W = LIBSVC(A,PROXM([],'R',1))
• a string with the name of the routine to compute the kernel from A
• a cell-array with this name and additional parameters.  This will be used for the evaluation of a dataset B by B*W or PRMAP(B,W) as  well.

If KERNEL = 0 (or not given) it is assumed that A is already the  kernelmatrix (square). In this also a kernel matrix should be supplied at  evaluation by B*W or PRMAP(B,W). However, the kernel has to be computed with  respect to support objects listed in J (the order of objects in J does matter).

Reference(s)

R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using the second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005

See also

mappings, datasets, libsvc, svc, proxm,