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lib/Algorithm/SVMLight.pm  view on Meta::CPAN

optimization algorithms used in SVMlight are described in [Joachims,
2002a ]. [Joachims, 1999a]. The algorithm has scalable memory
requirements and can handle problems with many thousands of support
vectors efficiently.

 -- http://svmlight.joachims.org/

=back

Support Vector Machines in general, and SVMLight specifically,
represent some of the best-performing Machine Learning approaches in
domains such as text categorization, image recognition, bioinformatics
string processing, and others.

For efficiency reasons, the underlying SVMLight engine indexes features by integers, not
strings.  Since features are commonly thought of by name (e.g. the
words in a document, or mnemonic representations of engineered
features), we provide in C<Algorithm::SVMLight> a simple mechanism for
mapping back and forth between feature names (strings) and feature
indices (integers).  If you want to use this mechanism, use the
C<add_instance()> and C<predict()> methods.  If not, use the

lib/Algorithm/SVMLight.pm  view on Meta::CPAN

=item is_trained()

Returns a boolean value indicating whether or not C<train()> has been
called on this model.

=item predict(attributes => \%y)

After C<train()> has been called, the model may be applied to
previously-unseen combinations of attributes.  The C<predict()> method
accepts an C<attributes> parameter just like C<add_instance()>, and
returns its best prediction of the label that would apply to the given
attributes.  The sign of the returned label (positive or negative)
indicates whether the new instance is considered a positive or
negative instance, and the magnitude of the label corresponds in some
way to the confidence with which the model is making that assertion.

=item predict_i(\@indices, \@values)

This is just like C<predict()>, but bypasses all the string-to-integer
mapping of feature names.  See also C<add_instance_i()>.