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java.lang.Objectedu.stanford.nlp.classify.AbstractLinearClassifierFactory<L,F>
edu.stanford.nlp.classify.NBLinearClassifierFactory<L,F>
L
- The type of the labels in the ClassifierF
- The type of the features in the Classifierpublic class NBLinearClassifierFactory<L,F>
Provides a medium-weight implementation of Bernoulli (or binary) Naive Bayes via a linear classifier. It's medium weight in that it uses dense arrays for counts and calculation (but, hey, NB is efficient to estimate). Each feature is treated as an independent binary variable.
CDM Jun 2003: I added a dirty trick so that if there is a feature that is always on in input examples, then its weight is turned into a prior feature! (This will work well iff it is also always on at test time.) In fact, this is done for each such feature, so by having several such features, one can even get an integral prior boost out of this.
Constructor Summary | |
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NBLinearClassifierFactory()
Create a ClassifierFactory. |
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NBLinearClassifierFactory(double sigma)
Create a ClassifierFactory. |
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NBLinearClassifierFactory(double sigma,
boolean interpretAlwaysOnFeatureAsPrior)
Create a ClassifierFactory. |
Method Summary | |
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void |
setTuneSigmaCV(int folds)
setTuneSigmaCV sets the tuneSigma flag: when turned on,
the sigma is tuned by cross-validation. |
protected double[][] |
trainWeights(GeneralDataset<L,F> data)
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Methods inherited from class edu.stanford.nlp.classify.AbstractLinearClassifierFactory |
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trainClassifier, trainClassifier, trainClassifier, trainClassifier |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public NBLinearClassifierFactory()
public NBLinearClassifierFactory(double sigma)
sigma
- The amount of add-sigma smoothing of evidencepublic NBLinearClassifierFactory(double sigma, boolean interpretAlwaysOnFeatureAsPrior)
sigma
- The amount of add-sigma smoothing of evidenceinterpretAlwaysOnFeatureAsPrior
- If true, a feature that is in every
data item is interpreted as an indication to include a prior
factor over classes. (If there are multiple such features, an
integral "prior boost" will occur.) If false, an always on
feature is interpreted as an evidence feature (and, following
the standard math) will have no effect on the model.Method Detail |
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protected double[][] trainWeights(GeneralDataset<L,F> data)
trainWeights
in class AbstractLinearClassifierFactory<L,F>
public void setTuneSigmaCV(int folds)
tuneSigma
flag: when turned on,
the sigma is tuned by cross-validation.
If there is less data than the number of folds, leave-one-out is used.
The default for tuneSigma is false.
folds
- Number of folds for cross validation
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