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See:
Description
Interface Summary | |
Boostable | This interface is a tag indicating that the classifier attends to the InstanceList.getInstanceWeight() weights when training. |
ClassifierEvaluating |
Class Summary | |
AbstractClassifierEvaluating | Created: Apr 13, 2005 |
AccuracyEvaluator | |
AdaBoost | AdaBoost Robert E. |
AdaBoostM2 | AdaBoostM2 |
AdaBoostM2Trainer | This version of AdaBoost can handle multi-class problems. |
AdaBoostTrainer | This version of AdaBoost should be used only for binary classification. |
BaggingClassifier | |
BaggingTrainer | Bagging Trainer. |
BalancedWinnow | Classification methods of BalancedWinnow algorithm. |
BalancedWinnowTrainer | An implementation of the training methods of a BalancedWinnow on-line classifier. |
C45 | A C4.5 Decision Tree classifier. |
C45.Node | |
C45Trainer | A C4.5 decision tree learner, approximtely. |
Classification | The result of classifying a single instance. |
Classifier | Abstract parent of all Classifiers. |
ClassifierTrainer | Abstract parent of all classifier trainers. |
ConfidencePredictingClassifier | |
ConfidencePredictingClassifierTrainer | |
DecisionTree | Decision Tree classifier. |
DecisionTree.Node | |
DecisionTreeTrainer | A decision tree learner, roughly ID3. |
FeatureSelectingClassifierTrainer | Adaptor for adding feature selection to a classifier trainer. |
IncrementalClassifierTrainer | Adds the notion of incremental training to a ClassifierTrainer, through the availability of incrementalTrain() methods, which parallel the train() methods. |
MaxEnt | Maximum Entropy classifier. |
MaxEntTrainer | The trainer for a Maximum Entropy classifier. |
MCMaxEnt | Maximum Entropy classifier. |
MCMaxEntTrainer | The trainer for a Maximum Entropy classifier. |
NaiveBayes | A classifier that classifies instances according to the NaiveBayes method. |
NaiveBayesTrainer | Class used to generate a NaiveBayes classifier from a set of training data. |
Trial | A convenience class for running an instance list through a classifier and storing the instancelist, the classifier, and the resulting classifications of each instance. |
TUI | Text User Interface for classification (unsupported). |
Winnow | Classification methods of Winnow2 algorithm. |
WinnowTrainer | An implementation of the training methods of a Winnow2 on-line classifier. |
Classes for training and classifying instances. All classification techniques in MALLET are implemented as two classes: a trainer and a classifier. The trainer injests the training data and creates a classifier that holds the parameters set during training. The classifier applies those parameters to an Instance to produce a classification of the Instance.
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