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    For example,

      ...
      $me->cut(1)

    will cut all features that occur less than one time.

  learn
    Learn a model from all the samples that the learner have seen so far,
    returns an AI::MaxEntropy::Model object, which can be used to make
    prediction on unlabeled samples.

      ...

      my $model = $me->learn;

      print $model->predict(['x1', 'x2', ...]);

PROPERTIES
  algorithm
    This property enables client program to choose different algorithms for
    learning the ME model and set their parameters.

    There are mainly 3 algorithm for learning ME models, they are GIS, IIS
    and L-BFGS. This module implements 2 of them, namely, L-BFGS and GIS.
    L-BFGS provides full functionality, while GIS runs faster, but only
    applicable on limited scenarios.

    To use GIS, the following conditions must be satisified:

    1. All samples have same number of active features

    2. No feature has been cut

    3. No smoother is used (in fact, the property "smoother" is simplly
    ignored when the type of algorithm equal to 'gis').

    This property "algorithm" is supposed to be a hash ref, like

      {
        type => ...,
        progress_cb => ...,
        param_1 => ...,
        param_2 => ...,
        ...,
        param_n => ...
      }

   type
    The entry "type => ..." specifies which algorithm is used for the
    optimization. Valid values include:

      'lbfgs'       Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)
      'gis'         General Iterative Scaling (GIS)

    If ommited, 'lbfgs' is used by default.

   progress_cb
    The entry "progress_cb => ..." specifies the progress callback
    subroutine which is used to trace the process of the algorithm. The
    specified callback routine will be called at each iteration of the
    algorithm.

    For L-BFGS, "progress_cb" will be directly passed to "fmin" in
    Algorithm::LBFGS. f(x) is the negative log-likelihood of current lambda
    vector.

    For GIS, the "progress_cb" is supposed to have a prototype like

      progress_cb(i, lambda, d_lambda, lambda_norm, d_lambda_norm)

    "i" is the number of the iterations, "lambda" is an array ref containing
    the current lambda vector, "d_lambda" is an array ref containing the
    delta of the lambda vector in current iteration, "lambda_norm" and
    "d_lambda_norm" are Euclid norms of "lambda" and "d_lambda"
    respectively.

    For both L-BFGS and GIS, the client program can also pass a string
    '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.