NAME

       Bio::Tools::PSort::SVMLoc - Perl bindings for the libsvm Support Vector Machine library in Psortb.

SYNOPSIS

         use Bio::Tools::PSort::SVMLoc;

         # Load the model stored in the file 'sample.model' and Frequent Patterns in 'FreqPatt'
         $svm = new Bio::Tools::PSort::SVMLoc(Model => 'sample.model', FreqPattern => 'FreqPatt');

         # Classify a sequence in $seq, do not include FASTA headers!
         $svm->classify($seq);

DESCRIPTION

       Bio::Tools::PSort::SVMLoc implements a Support Vector Machine for Perl.  Support Vector Machines provide
       a method for creating classifcation functions from a set of labeled training data, from which predictions
       can be made for subsequent data sets.

       Bio::Tools::PSort::SVMLoc is a stripped down version of Algorithm::SVM design specifically for PSortb.
       If you need functionality such as training of SVM, please use that module instead.

CONSTRUCTOR

         # Load an existing SVM.
         $svm = new Bio::Tools::PSort::SVMLoc(Model  => 'sample.model', FreqPattern => 'FreqPatt');

       A Bio::Tools::PSort::SVMLoc object must be created loaded from an existing model file.

       An existing SVM is loaded from a file using the Model named parameter and the frequent patterns set.  The
       model file should be of the format produced by the svm->train program (distributed with the libsvm
       library) or from the $svm->save() method in Algorithm::SVM.

METHODS

         $result = $svm->classify($seq);

       The classify method is used to classify a set of data according to the loaded model.  The method accepts
       a single parameter, which should be a protein sequence object.  Returns a floating point number
       corresponding to the predicted value.

MAINTAINER

       Matthew Laird <matt@brinkman.mbb.sfu.ca>

SEE ALSO

       The libsvm homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/

ACKNOWLEDGEMENTS

       Thanks go out to Fiona Brinkman and the other members of the Simon Fraser University Brinkman Laboratory
       for providing me the opportunity to develop this module.  Additional thanks go to Chih-Jen Lin, one of
       the libsvm authors, for being particularly helpful during the development process.