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=head3 parameters

The rest entries are parameters for the specified algorithm.
Each parameter will be assigned with its default value when it is not
given explicitly.

For L-BFGS, the parameters will be directly passed to
L<Algorithm::LBFGS> object, please refer to L<Algorithm::LBFGS/Parameters>
for details.

For GIS, there is only one parameter C<epsilon>, which controls the
precision of the algorithm (similar to the C<epsilon> in
L<Algorithm::LBFGS>). Generally speaking, a smaller C<epsilon> produces
a more precise result. The default value of C<epsilon> is 1e-3.

=head2 smoother

The smoother is a solution to the over-fitting problem. 
This property chooses which type of smoother the client program want to
apply and sets the smoothing parameters. 

Only one smoother have been implemented in this version of the module, 
the Gaussian smoother.

One can apply the Gaussian smoother as following,

  my $me = AI::MaxEntropy->new(
      smoother => { type => 'gaussian', sigma => 0.6 }
  );

The parameter C<sigma> indicates the strength of smoothing.
Usually, sigma is a positive number no greater than 1.0.
The strength of smoothing grows as sigma getting close to 0.

=head1 SEE ALSO

L<AI::MaxEntropy::Model>, L<AI::MaxEntropy::Util>

L<Algorithm::LBFGS>

L<Statistics::MaxEntropy>, L<Algorithm::CRF>, L<Algorithm::SVM>,
L<AI::DecisionTree>

=head1 AUTHOR

Laye Suen, E<lt>laye@cpan.orgE<gt>

=head1 COPYRIGHT AND LICENSE

The MIT License

Copyright (C) 2008, Laye Suen

Permission is hereby granted, free of charge, to any person obtaining a 
copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.

=head1 REFERENCE

=over

=item
A. L. Berge, V. J. Della Pietra, S. A. Della Pietra. 
A Maximum Entropy Approach to Natural Language Processing,
Computational Linguistics, 1996.

=item
S. F. Chen, R. Rosenfeld.
A Gaussian Prior for Smoothing Maximum Entropy Models,
February 1999 CMU-CS-99-108.

=back