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    'verbose' to "progress_cb" to use a default progress callback which
    simply print out the progress on the screen.

    "progress_cb" can also be omitted if the client program do not want to
    trace the progress.

   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 Algorithm::LBFGS
    object, please refer to "Parameters" in Algorithm::LBFGS for details.

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

  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 "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.

SEE ALSO
    AI::MaxEntropy::Model, AI::MaxEntropy::Util

    Algorithm::LBFGS

    Statistics::MaxEntropy, Algorithm::CRF, Algorithm::SVM, AI::DecisionTree

AUTHOR
    Laye Suen, <laye@cpan.org>

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.

REFERENCE
    A. L. Berge, V. J. Della Pietra, S. A. Della Pietra. A Maximum Entropy
    Approach to Natural Language Processing, Computational Linguistics,
    1996.
    S. F. Chen, R. Rosenfeld. A Gaussian Prior for Smoothing Maximum Entropy
    Models, February 1999 CMU-CS-99-108.