Package edu.stanford.nlp.classify

Interface Summary

Interface	Description
Classifier<L,F>	A simple interface for classifying and scoring data points, implemented by most of the classifiers in this package.
ClassifierCreator<L,F>	Creates a classifier with given weights
ClassifierFactory<L,F,C extends Classifier<L,F>>	A simple interface for training a Classifier from a Dataset of training examples.
ProbabilisticClassifier<L,F>
ProbabilisticClassifierCreator<L,F>	Creates a probablic classifier with given weights
RVFClassifier<L,F>	A simple interface for classifying and scoring data points with real-valued features.

Class Summary

Class	Description
AbstractLinearClassifierFactory<L,F>	Shared methods for training a LinearClassifier.
AdaptedGaussianPriorObjectiveFunction<L,F>	Adapt the mean of the Gaussian Prior by shifting the mean to the previously trained weights
BiasedLogConditionalObjectiveFunction	Maximizes the conditional likelihood with a given prior.
BiasedLogisticObjectiveFunction
CrossValidator<L,F>	This class is meant to simplify performing cross validation of classifiers for hyper-parameters.
CrossValidator.SavedState
Dataset<L,F>	An interfacing class for ClassifierFactory that incrementally builds a more memory-efficient representation of a List of Datum objects for the purposes of training a Classifier with a ClassifierFactory.
GeneralDataset<L,F>	The purpose of this interface is to unify Dataset and RVFDataset.
GeneralizedExpectationObjectiveFunction<L,F>	Implementation of Generalized Expectation Objective function for an I.I.D.
LinearClassifier<L,F>	Implements a multiclass linear classifier.
LinearClassifierFactory<L,F>	Builds various types of linear classifiers, with functionality for setting objective function, optimization method, and other parameters.
LinearClassifierFactory.LinearClassifierCreator<L,F>
LogConditionalObjectiveFunction<L,F>	Maximizes the conditional likelihood with a given prior.
LogisticClassifier<L,F>	A classifier for binary logistic regression problems.
LogisticClassifierFactory<L,F>	Builds a classifier for binary logistic regression problems.
LogisticObjectiveFunction	Maximizes the conditional likelihood with a given prior.
LogPrior	A Prior for functions.
NBLinearClassifierFactory<L,F>	Provides a medium-weight implementation of Bernoulli (or binary) Naive Bayes via a linear classifier.
PRCurve	A class to create recall-precision curves given scores used to fit the best monotonic function for logistic regression and SVMs.
RVFDataset<L,F>	An interfacing class for ClassifierFactory that incrementally builds a more memory-efficient representation of a List of RVFDatum objects for the purposes of training a Classifier with a ClassifierFactory.
SemiSupervisedLogConditionalObjectiveFunction	Maximizes the conditional likelihood with a given prior.
SVMLightClassifier<L,F>	This class represents a trained SVM Classifier.
SVMLightClassifierFactory<L,F>	This class is meant for training SVMs (SVMLightClassifiers).
WeightedDataset<L,F>

Enum Summary

Enum	Description
LogPrior.LogPriorType