Changes for version 0.04 - 2002-11-07
- Added learners for SVMs, Decision Trees, and a pass-through to Weka.
- Added a virtual class for binary classifiers.
- Wrote documentation for lots of the undocumented classes.
- Added a PNG file giving an overview diagram of the classes.
- Added a script 'categorizer' to provide a simple command-line interface to AI::Categorizer
- save_state() and restore_state() now save to a directory, not a file.
- Removed F1(), precision(), recall(), etc. from Util package since they're in Statistics::Contingency. Added random_elements() to Util.
- Collection::Files now warns when no category information is known about a document in the collection (knowing it's in zero categories is okay).
- Added the Collection::InMemory class
- Much more thorough testing with 'make test'.
- Added add_hypothesis() method to Experiment.
- Added dot() and value() methods to FeatureVector.
- Added 'feature_selection' parameter to KnowledgeSet.
- Added document($name) accessor method to KnowledgeSet.
- In KnowledgeSet, load(), read(), and scan_*() can now accept a Collection object.
- Added document_frequency(), finish(), and weigh_features() methods to KnowledgeSet.
- Added save_features() and restore_features() to KnowledgeSet.
- Added default categories() and categorize() methods to Learner base class. get_scores() is now abstract.
- Extended interface of ObjectSet class with retrieve(), includes(), and includes_name().
- Moved 'term_weighting' parameter from Document to KnowledgeSet, since the normalized version needs to know the maximum term-frequency. Also changed its values to 'n', 'l', 'b', and 't', with 'x' a synonym for 't'.
- Implemented full range of TF/IDF term weighting methods (see Salton & Buckley, "Term Weighting Approaches in Automatic Text Retrieval", in journal "Information Processing & Management", 1988 #5)
Modules
Automatic Text Categorization
A named category of documents
Access stored documents
Embodies a document
Coordinate experimental results
Features vs. Values
Embodies a set of category assignments
Encapsulates set of documents
Abstract Machine Learner Class
Abstract class for boolean categorizers
Decision Tree Learner
Naive Bayes Algorithm For AI::Categorizer
Support Vector Machine Learner
Pass-through wrapper to Weka system
Saving and Restoring State
Provides
in lib/AI/Categorizer/Collection/DBI.pm
in lib/AI/Categorizer/Collection/Files.pm
in lib/AI/Categorizer/Collection/InMemory.pm
in lib/AI/Categorizer/Collection/SingleFile.pm
in lib/AI/Categorizer/Document/SMART.pm
in lib/AI/Categorizer/Document/Text.pm
in lib/AI/Categorizer/ObjectSet.pm
in lib/AI/Categorizer/Util.pm