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 Author: Jiuan-Ru Jennifer Lai Date: Mar 18, 2008 11:53 Hi, I used the Categorizer::SVM library for large data classification (great tool); however, I'm having trouble analyzing the result from the SVM learner. - Categories have scores of either 0 or 1, with 1 being that this document belongs to this category, and 0 otherwise. Are there any scores representing probabilities or confidence level of belong to certain category other than these 0, 1 values? - Suppose this document could belong to 3 possible categories: cat1, cat2, and cat3. The best_category method simply picks the first category as the classification decision. If you call, $hypothesis->categories, the categories outputed don't seem to be in the order of probabilities or confidence level. They seem to be in the fixed order....and whatever listed first is favored. I hope someone can clear my confusion on the scores of categories in the SVM module. Thank you very much in advance, Jennifer no comments  Author: Hester Newton Date: Jul 15, 2007 07:57 Big News For SZSN! Shares Rocket! UP 37.5%% Shandong Zhouyuan Seed and Nursery Co., Ltd (SZSN)$0.33 UP 37.5%% SZSN new releases show huge expansion and Multi-Million dollar projects. Share prices rocket! Friday's trading was strong. Get On SZSN first thing Monday! Yes, it is that good. If they have work and family commitments, then perhaps it is easier to set aside a weekend to see a large number of bands in one go than go to regular gigs. He was detained by police in Middlesex on Saturday in connection with failing to attend a court hearing over alleged drugs offences in Glasgow. Show full article (1.21Kb) no comments
 Author: Ignacio J. Ortega Lopera Date: Jun 7, 2007 00:46 It's possible to add training to a learner? how? What i try is to reopen a state file, and add new documents to the training set without reading the entire corpus again.. It's seems that Algorithm::NativeBayes has a "purge" parameter that seems to help doing that, it permit add new instances, after a train.. Saludios, Ignacio J. Ortega no comments
 Author: Robert Barta Date: Jun 4, 2007 09:08 Hi, I seem to have problems with umlauts, such as in words Präsentation When a document is added with return new AI::Categorizer::Document(name => $filename, content =>$content); to the collection, after loading and finish, the feature vector contains only fragments of these words, such as pr => 1 sentation => 1 Setting the locale on the shell or in Perl does not have any effect use locale; not even with turning on de_AT explicitly. -- Aaaaaah, lib/AI/Categorizer/Document.pm is NOT using locale and use locale is very, uhm, local %%-) Patching the file does not seem to break the test cases. \rho no comments
 Author: Ignacio J. Ortega Lopera Date: May 30, 2007 09:48 Hola a todos: When trying to use i've found that DBI try to read categories from database, it trie to read a second column that seems to contain categories, it uses something like [$result[1]].., my perl knowledge is a little poor to say the least.. but it seems to my that code later expects this parameter as an array of Category objects.. I've done a little change that permits that this second column be a list of categories separated by commas, code ( can be a diff -u if needed) attached, maybe it's usefull to anyone.. Thanks for that package, Ken, it's ... wonderful :).. Saludos, Ignacio J. Ortega --------------------------------------------------------- Technical Manager http://www.derecho.com/ no comments  Author: Jhoon Date: May 25, 2007 02:58 Hello, I’d like to select more important features using AI::Categorizer, and so modified demo.pl as follows === FROM === my$k = AI::Categorizer::KnowledgeSet->new( verbose => 1 ); === TO === my $k = AI::Categorizer::KnowledgeSet->new( verbose => 1, feature_selector => new AI::Categorizer::FeatureSelector::DocFrequency( verbose => 1, features_kept => 1000 ) ); === END === I observed the performance according to change the value of features_kept, but the performance is always same. I’d appreciate it if you tell me how to do the feature selection using AI::Categorizer? Thank you very much in advance. Jae-Hoon. no comments  Author: Jianmin WU Date: May 19, 2007 05:42 hi, buddies, I am not sure if i am in the right place. :-) I am a fresh man to the perl and perl AI module. I am trying to do the NaiveBayes experiments with the help of code demo.pl in example of the module of AI::Categorizer. Now I am confused about how to do the feature selection. The documents say that KnowledgeSet::load( ) will do feature selection and read the corpus at the same time. So, I change the construction of KnowledgeSet in demo.pl from my$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 ) Show full article (1.00Kb) 1 Comment