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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