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Trainable classifier: LIBSVM, nu-algorithme


 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.

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


Optimises a support vector classifier for the dataset A by the libsvm  package, see 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).


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,

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PRTools User Guide

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