AI::Categorizer - Automatic Text Categorization River stage one • 1 direct dependent • 1 total dependent

"AI::Categorizer" is a framework for automatic text categorization. It consists of a collection of Perl modules that implement common categorization tasks, and a set of defined relationships among those modules. The various details are flexible - for...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Learner - Abstract Machine Learner Class River stage one • 1 direct dependent • 1 total dependent

The "AI::Categorizer::Learner" class is an abstract class that will never actually be directly used in your code. Instead, you will use a subclass like "AI::Categorizer::Learner::NaiveBayes" which implements an actual machine learning algorithm. The ...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Storable - Saving and Restoring State River stage one • 1 direct dependent • 1 total dependent

This class implements methods for storing the state of an object to a file and restoring from that file later. In "AI::Categorizer" it is generally used in order to let data persist across multiple invocations of a program....

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Category - A named category of documents River stage one • 1 direct dependent • 1 total dependent

This simple class represents a named category which may contain zero or more documents. Each category is a "singleton" by name, so two Category objects with the same name should not be created at once....

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Document - Embodies a document River stage one • 1 direct dependent • 1 total dependent

The Document class embodies the data in a single document, and contains methods for turning this data into a FeatureVector. Usually documents are plain text, but subclasses of the Document class may handle any kind of data....

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Hypothesis - Embodies a set of category assignments River stage one • 1 direct dependent • 1 total dependent

A Hypothesis embodies a set of category assignments that a categorizer makes about a single document. Because one may be interested in knowing different kinds of things about the assignments (for instance, what categories were assigned, which categor...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Collection - Access stored documents River stage one • 1 direct dependent • 1 total dependent

This abstract class implements an iterator for accessing documents in their natively stored format. You cannot directly create an instance of the Collection class, because it is abstract - see the documentation for the "Files", "SingleFile", or "InMe...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Experiment - Coordinate experimental results River stage one • 1 direct dependent • 1 total dependent

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 cor...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Learner::SVM - Support Vector Machine Learner River stage one • 1 direct dependent • 1 total dependent

This class implements a Support Vector Machine machine learner, using Cory Spencer's "Algorithm::SVM" module. In lots of the recent academic literature, SVMs perform very well for text categorization....

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::KnowledgeSet - Encapsulates set of documents River stage one • 1 direct dependent • 1 total dependent

The KnowledgeSet class that provides an interface to a set of documents, a set of categories, and a mapping between the two. Many parameters for controlling the processing of documents are managed by the KnowledgeSet class....

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Learner::Weka - Pass-through wrapper to Weka system River stage one • 1 direct dependent • 1 total dependent

This class doesn't implement any machine learners of its own, it merely passes the data through to the Weka machine learning system (http://www.cs.waikato.ac.nz/~ml/weka/). This can give you access to a collection of machine learning algorithms not o...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::FeatureVector - Features vs. Values River stage one • 1 direct dependent • 1 total dependent

This class implements a "feature vector", which is a flat data structure indicating the values associated with a set of features. At its base level, a FeatureVector usually represents the set of words in a document, with the value for each feature in...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::FeatureSelector - Abstract Feature Selection class River stage one • 1 direct dependent • 1 total dependent

The KnowledgeSet class that provides an interface to a set of documents, a set of categories, and a mapping between the two. Many parameters for controlling the processing of documents are managed by the KnowledgeSet class....

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Learner::Boolean - Abstract class for boolean categorizers River stage one • 1 direct dependent • 1 total dependent

This is an abstract class which turns boolean categorizers (categorizers based on algorithms that can just provide yes/no categorization decisions for a single document and single category) into multi-valued categorizers. For instance, the decision t...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Learner::Guesser - Simple guessing based on class probabilities River stage one • 1 direct dependent • 1 total dependent

This implements a simple category guesser that makes assignments based solely on the prior probabilities of categories. For instance, if 5% of the training documents belong to a certain category, then the probability of any test document being assign...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Collection::Files - One document per file River stage one • 1 direct dependent • 1 total dependent

This implements a Collection class in which each document exists as a single file on a filesystem. The documents can exist in a single directory, or in several directories....

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Learner::KNN - K Nearest Neighbour Algorithm For AI::Categorizer River stage one • 1 direct dependent • 1 total dependent

This is an implementation of the k-Nearest-Neighbor decision-making algorithm, applied to the task of document categorization (as defined by the AI::Categorizer module). See AI::Categorizer for a complete description of the interface....

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Learner::NaiveBayes - Naive Bayes Algorithm For AI::Categorizer River stage one • 1 direct dependent • 1 total dependent

This is an implementation of the Naive Bayes decision-making algorithm, applied to the task of document categorization (as defined by the AI::Categorizer module). See AI::Categorizer for a complete description of the interface. This module is now a w...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::Learner::DecisionTree - Decision Tree Learner River stage one • 1 direct dependent • 1 total dependent

This class implements a Decision Tree machine learner, using "AI::DecisionTree" to do the internal work....

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC

AI::Categorizer::FeatureSelector::ChiSquare - ChiSquare Feature Selection class River stage one • 1 direct dependent • 1 total dependent

Feature selection with the ChiSquare function. Chi-Square(t,ci) = (N.(AD-CB)^2) ----------------------- (A+C).(B+D).(A+B).(C+D) where t = term ci = category i N = number of documents in the collection A = number of times where t and c co-occur B = " ...

KWILLIAMS/AI-Categorizer-0.09 - 24 Mar 2007 02:39:15 UTC
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