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Packages that use de.fu_berlin.ties.classify | |
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de.fu_berlin.ties.classify | This package provides functionality for classification of texts and feature vectors. |
de.fu_berlin.ties.classify.winnow | This package contains the Winnow classification algorithm and related algorithms and classes. |
de.fu_berlin.ties.combi | This package provides combination strategies for combining sequential classification decisions. |
de.fu_berlin.ties.demo | This package contains demo code for showing how the system works. |
de.fu_berlin.ties.extract | This package handles information extraction and entity recognition. |
de.fu_berlin.ties.extract.amend | This package provides code for reanalysing proposed extractions and performing suitable amendments to improve results. |
de.fu_berlin.ties.filter | This packages provides generic filtering and rewriting functionality. |
Classes in de.fu_berlin.ties.classify used by de.fu_berlin.ties.classify | |
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Classifier
Classes implementing this interface must be able to classify items represented by feature vectors. |
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Prediction
A prediction, wrapping the predicted class and the probability of the prediction. |
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PredictionDistribution
A distribution over the classes predicted by a classifier. |
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Probability
Wraps a probability. |
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Reranker
Reranks the predictions in a distribution by multiplying the probabilities of each of them with a bias, if specified for the type of the prediction. |
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TrainableClassifier
Classifiers extending this abstract class must provide a training mechanism by implementing the TrainableClassifier.doTrain(FeatureVector, String, ContextMap)
method. |
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Tuner
This class provides support for iterative training, also called TUNE (Train-until-no-errors) training. |
Classes in de.fu_berlin.ties.classify used by de.fu_berlin.ties.classify.winnow | |
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Classifier
Classes implementing this interface must be able to classify items represented by feature vectors. |
|
Prediction
A prediction, wrapping the predicted class and the probability of the prediction. |
|
PredictionDistribution
A distribution over the classes predicted by a classifier. |
|
TrainableClassifier
Classifiers extending this abstract class must provide a training mechanism by implementing the TrainableClassifier.doTrain(FeatureVector, String, ContextMap)
method. |
Classes in de.fu_berlin.ties.classify used by de.fu_berlin.ties.combi | |
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PredictionDistribution
A distribution over the classes predicted by a classifier. |
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Probability
Wraps a probability. |
Classes in de.fu_berlin.ties.classify used by de.fu_berlin.ties.demo | |
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PredictionDistribution
A distribution over the classes predicted by a classifier. |
Classes in de.fu_berlin.ties.classify used by de.fu_berlin.ties.extract | |
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Classifier
Classes implementing this interface must be able to classify items represented by feature vectors. |
|
Prediction
A prediction, wrapping the predicted class and the probability of the prediction. |
|
Probability
Wraps a probability. |
|
Reranker
Reranks the predictions in a distribution by multiplying the probabilities of each of them with a bias, if specified for the type of the prediction. |
|
TrainableClassifier
Classifiers extending this abstract class must provide a training mechanism by implementing the TrainableClassifier.doTrain(FeatureVector, String, ContextMap)
method. |
|
Tuner
This class provides support for iterative training, also called TUNE (Train-until-no-errors) training. |
Classes in de.fu_berlin.ties.classify used by de.fu_berlin.ties.extract.amend | |
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Prediction
A prediction, wrapping the predicted class and the probability of the prediction. |
Classes in de.fu_berlin.ties.classify used by de.fu_berlin.ties.filter | |
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PredictionDistribution
A distribution over the classes predicted by a classifier. |
|
Reranker
Reranks the predictions in a distribution by multiplying the probabilities of each of them with a bias, if specified for the type of the prediction. |
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