Hi,
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AI-Categorizer-0.04.tar.gz
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Changes since 0.03:
- 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)
-Ken
