cosmo classify libsvm hdrΒΆ

function predicted=cosmo_classify_libsvm(samples_train, targets_train, samples_test, opt)
% libsvm-based SVM classifier
%
% predicted=cosmo_classify_libsvm(samples_train, targets_train, samples_test, opt)
%
% Inputs
%   samples_train      PxR training data for P samples and R features
%   targets_train      Px1 training data classes
%   samples_test       QxR test data
%   opt                (optional) struct with options for svmtrain
%     .autoscale       If true (default), z-scoring is done on the training
%                      set; the test set is z-scored using the mean and std
%                      estimates from the training set.
%     ?                any option supported by libsvm's svmtrain.
%
% Output
%   predicted          Qx1 predicted data classes for samples_test
%
% Notes:
%  - this function requires libsvm version 3.18 or later:
%    https://github.com/cjlin1/libsvm
%  - by default a linear kernel is used ('-t 0')
%  - this function uses LIBSVM's svmtrain function, which has the same
%    name as matlab's builtin version. Use of this function is not
%    supported when matlab's svmtrain precedes in the matlab path; in
%    that case, adjust the path or use cosmo_classify_matlabsvm instead.
%  - for a guide on svm classification, see
%      http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
%  - By default this function performs z-scoring of the data. To switch
%    this off, set 'autoscale' to false
%  - cosmo_crossvalidate and cosmo_crossvalidation_measure
%    provide an option 'normalization' to perform data scaling
%
%
% See also svmtrain, svmclassify, cosmo_classify_svm,
%          cosmo_classify_matlabsvm
%
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #