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    for ensemble learning. No timetable for their creation has yet been set.

    Please see the documentation of these individual modules for more details on
    their guts and quirks. See the "AI::Categorizer::Learner" documentation for
    a description of the general categorizer interface.

    If you wish to create your own classifier, you should inherit from
    "AI::Categorizer::Learner" or "AI::Categorizer::Learner::Boolean", which are
    abstract classes that manage some of the work for you.

  Feature Vectors

    Most categorization algorithms don't deal directly with documents' data,
    they instead deal with a *vector representation* of a document's *features*.
    The features may be any properties of the document that seem helpful for
    determining its category, but they are usually some version of the "most
    important" words in the document. A list of features and their weights in
    each document is encapsulated by the "AI::Categorizer::FeatureVector" class.
    You may think of this class as roughly analogous to a Perl hash, where the
    keys are the names of features and the values are their weights.

  Hypotheses

    The result of asking a categorizer to categorize a previously unseen
    document is called a hypothesis, because it is some kind of "statistical
    guess" of what categories this document should be assigned to. Since you may
    be interested in any of several pieces of information about the hypothesis
    (for instance, which categories were assigned, which category was the single
    most likely category, the scores assigned to each category, etc.), the
    hypothesis is returned as an object of the "AI::Categorizer::Hypothesis"
    class, and you can use its object methods to get information about the
    hypothesis. See its class documentation for the details.

  Experiments

    The "AI::Categorizer::Experiment" class helps you organize the results of
    categorization experiments. As you get lots of categorization results
    (Hypotheses) back from the Learner, you can feed these results to the
    Experiment class, along with the correct answers. When all results have been
    collected, you can get a report on accuracy, precision, recall, F1, and so
    on, with both micro-averaging and macro-averaging over categories. We use
    the "Statistics::Contingency" module from CPAN to manage the calculations.
    See the docs for "AI::Categorizer::Experiment" for more details.

METHODS
    new()
        Creates a new Categorizer object and returns it. Accepts lots of
        parameters controlling behavior. In addition to the parameters listed
        here, you may pass any parameter accepted by any class that we create
        internally (the KnowledgeSet, Learner, Experiment, or Collection
        classes), or any class that *they* create. This is managed by the
        "Class::Container" module, so see its documentation for the details of
        how this works.

        The specific parameters accepted here are:

        progress_file
            A string that indicates a place where objects will be saved during
            several of the methods of this class. The default value is the
            string "save", which means files like "save-01-knowledge_set" will
            get created. The exact names of these files may change in future
            releases, since they're just used internally to resume where we last
            left off.

        verbose
            If true, a few status messages will be printed during execution.

        training_set
            Specifies the "path" parameter that will be fed to the
            KnowledgeSet's "scan_features()" and "read()" methods during our
            "scan_features()" and "read_training_set()" methods.

        test_set
            Specifies the "path" parameter that will be used when creating a
            Collection during the "evaluate_test_set()" method.

        data_root
            A shortcut for setting the "training_set", "test_set", and
            "category_file" parameters separately. Sets "training_set" to
            "$data_root/training", "test_set" to "$data_root/test", and
            "category_file" (used by some of the Collection classes) to
            "$data_root/cats.txt".

    learner()
        Returns the Learner object associated with this Categorizer. Before
        "train()", the Learner will of course not be trained yet.

    knowledge_set()
        Returns the KnowledgeSet object associated with this Categorizer. If
        "read_training_set()" has not yet been called, the KnowledgeSet will not
        yet be populated with any training data.

    run_experiment()
        Runs a complete experiment on the training and testing data, reporting
        the results on "STDOUT". Internally, this is just a shortcut for calling
        the "scan_features()", "read_training_set()", "train()", and
        "evaluate_test_set()" methods, then printing the value of the
        "stats_table()" method.

    scan_features()
        Scans the Collection specified in the "test_set" parameter to determine
        the set of features (words) that will be considered when training the
        Learner. Internally, this calls the "scan_features()" method of the
        KnowledgeSet, then saves a list of the KnowledgeSet's features for later
        use.

        This step is not strictly necessary, but it can dramatically reduce
        memory requirements if you scan for features before reading the entire
        corpus into memory.

    read_training_set()
        Populates the KnowledgeSet with the data specified in the "test_set"
        parameter. Internally, this calls the "read()" method of the
        KnowledgeSet. Returns the KnowledgeSet. Also saves the KnowledgeSet
        object for later use.

    train()
        Calls the Learner's "train()" method, passing it the KnowledgeSet
        created during "read_training_set()". Returns the Learner object. Also
        saves the Learner object for later use.

    evaluate_test_set()
        Creates a Collection based on the value of the "test_set" parameter, and
        calls the Learner's "categorize_collection()" method using this
        Collection. Returns the resultant Experiment object. Also saves the
        Experiment object for later use in the "stats_table()" method.

    stats_table()
        Returns the value of the Experiment's (as created by
        "evaluate_test_set()") "stats_table()" method. This is a string that
        shows various statistics about the accuracy/precision/recall/F1/etc. of
        the assignments made during testing.

HISTORY
    This module is a revised and redesigned version of the previous
    "AI::Categorize" module by the same author. Note the added 'r' in the new
    name. The older module has a different interface, and no attempt at backward
    compatibility has been made - that's why I changed the name.

    You can have both "AI::Categorize" and "AI::Categorizer" installed at the
    same time on the same machine, if you want. They don't know about each other
    or use conflicting namespaces.

AUTHOR
    Ken Williams <ken@mathforum.org>

    Discussion about this module can be directed to the perl-AI list at
    <perl-ai@perl.org>. For more info about the list, see
    http://lists.perl.org/showlist.cgi?name=perl-ai

REFERENCES
    An excellent introduction to the academic field of Text Categorization is
    Fabrizio Sebastiani's "Machine Learning in Automated Text Categorization":
    ACM Computing Surveys, Vol. 34, No. 1, March 2002, pp. 1-47.

COPYRIGHT
    Copyright 2000-2003 Ken Williams. All rights reserved.

    This distribution is free software; you can redistribute it and/or modify it
    under the same terms as Perl itself. These terms apply to every file in the
    distribution - if you have questions, please contact the author.