hi, buddies,

I am not sure if i am in the right place. :-)

I am a fresh man to the perl and perl AI module.

I am trying to do the NaiveBayes experiments with the help of code demo.pl in
example of the module of AI::Categorizer.
Now I am confused about how to do the feature selection.

The documents say that KnowledgeSet::load( ) will do feature selection and
read the corpus at the same time. So, I change the construction of
KnowledgeSet in
demo.pl from
my $k = AI::Categorizer::KnowledgeSet-> new( verbose => 1 );
$k-> load( collection => $training )
to
my $k = AI::Categorizer::KnowledgeSet-> new( verbose => 1 , features_kept =
5000 );
$k-> load( collection => $training )

Then I re-run the code with expection to keep the top 5000 features with
high Document Frequency.
But it seems that there is no difference as before.  do i misunderstand any
point ?

And also,  is there any smoothing method implemented in
AI::Categorizer::Learner::NaiveBayes ?

Thanks for your attention

Jianmin