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See:
Description
| Class Summary | |
|---|---|
| C4_5Classifier | A wrapper for Ross Quinlan's C4.5 decision tree-based classifier (see http://www.rulequest.com/Personal/) |
| C4_5ClassifierFactory | |
| ClassifierUtils | |
| CsliMaxentClassifierFactory | |
| ExternalClassifier | An extension for the standard Stanford Classifier class for use with external stand-alone classifiers. |
| ExternalClassifierFactory | An extension for the standard Stanford ClassifierFactory class for use with external stand-alone classifiers. |
| MaxentWindowClassifier | A class which implements the standard Stanford classifier interface, but uses a buffered context to build features |
| MaxentWindowClassifierFactory | A class which implements the standard Stanford classifier interface, but uses a buffered context to build features |
| NaiveBayesClassifierFactory | |
| NaiveNgramClassifier | A binary classifier which uses a single n-gram model, making class decision by comparing log-likelihood with a given threshold |
| NaiveNgramClassifierFactory | |
| NgramClassifier | A classifier which uses multiple n-gram models, making class decision based on best log-likelihood |
| NgramClassifierFactory | |
| SvmLightClassifier | A wrapper for Thorsten Joachims' SVMlight support vector machine-based classifier (see http://svmlight.joachims.org/) |
| SvmLightClassifierFactory | |
| WeightedWindowClassifier | A class which implements the standard Stanford classifier interface, but classifies in a naive way, by applying a fixed set of filter weights to the features (which must be numerical) and then checking the sum (or max) against a threshold. |
| WeightedWindowClassifierFactory | A class which implements the standard Stanford classifier interface, but classifies in a naive way, by applying a fixed set of filter weights to the features (which must be numerical) and then checking the sum (or max) against a threshold. |
| WindowBuffer | A 2-dimensional buffer which efficiently calculates the column rows/sums during the shift/add operation |
This package provides classes for machine learning/classification, implemented using the Stanford (Klein/Manning) underlying data structures.
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