AI-MaxEntropy

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  Features
    Generally, a feature is a binary function answers a yes-no question on a
    specified piece of data.

    For examples,

      "Is it a red apple?"

      "Is it a yellow banana?"

    If the answer is yes, we say this feature is active on that piece of
    data.

    In practise, a feature is usually represented as a tuple "<x, y>". For
    examples, the above two features can be represented as

      <red, apple>

      <yellow, banana>

  Samples

lib/AI/MaxEntropy.pm  view on Meta::CPAN

Generally, a feature is a binary function answers a yes-no question on a
specified piece of data. 

For examples, 

  "Is it a red apple?"

  "Is it a yellow banana?"

If the answer is yes,
we say this feature is active on that piece of data.

In practise, a feature is usually represented as
a tuple C<E<lt>x, yE<gt>>. For examples, the above two features can be
represented as

  <red, apple>

  <yellow, banana>

=head2 Samples



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