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