Uses of Class
edu.illinois.cs.cogcomp.lbjava.classify.Classifier

Packages that use Classifier

Package	Description
edu.illinois.cs.cogcomp.lbjava.classify
edu.illinois.cs.cogcomp.lbjava.infer
edu.illinois.cs.cogcomp.lbjava.learn
edu.illinois.cs.cogcomp.lbjava.util

Uses of Classifier in edu.illinois.cs.cogcomp.lbjava.classify

Subclasses of Classifier in edu.illinois.cs.cogcomp.lbjava.classify

Modifier and Type	Class and Description
class	FeatureVectorReturner This classifier expects FeatureVectors as input, and it simply returns them as output.
class	LabelVectorReturner This classifier expects a FeatureVector as input, and it returns the contents of its labels list in a new FeatureVector as output.
class	MultiValueComparer This classifier applies another classifier to the example object and returns a Boolean feature (with value "true" or "false") indicating whether a given feature value appeared in the output of the classifier.
class	ValueComparer This classifier applies another classifier to the example object and returns a Boolean feature (with value "true" or "false") representing the equality of the argument classifier's feature value to a given value.

Fields in edu.illinois.cs.cogcomp.lbjava.classify declared as Classifier

Modifier and Type	Field and Description
protected Classifier	ValueComparer.labeler The classifier whose value will be compared.

Methods in edu.illinois.cs.cogcomp.lbjava.classify with parameters of type Classifier

Modifier and Type	Method and Description
Feature	RealFeature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
abstract Feature	Feature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
Feature	DiscreteFeature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	Feature.conjunctWith(DiscreteFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	DiscreteFeature.conjunctWith(DiscreteFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	Feature.conjunctWith(RealFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
void	ValueComparer.setLabeler(Classifier l) Sets the value of ValueComparer.labeler.
static double	Classifier.test(Classifier subject, Classifier oracle, Object[] o) Measures the performance of a classifier as compared with the values produced by an oracle.
static TestDiscrete	TestDiscrete.testDiscrete(Classifier classifier, Classifier oracle, Parser parser) Tests the given discrete classifier against the given oracle using the given parser to provide the labeled testing data.
static TestDiscrete	TestDiscrete.testDiscrete(TestDiscrete tester, Classifier classifier, Classifier oracle, Parser parser, boolean output, int outputGranularity) Tests the given discrete classifier against the given oracle using the given parser to provide the labeled testing data.
static void	TestReal.testReal(TestReal tester, Classifier classifier, Classifier oracle, Parser parser, boolean output, int outputGranularity) Tests the given real classifier against the given oracle using the given Parser to provide the real labeled testing data.

Constructors in edu.illinois.cs.cogcomp.lbjava.classify with parameters of type Classifier

Constructor and Description
DiscreteConjunctiveFeature(Classifier c, DiscreteFeature l, DiscreteFeature r) Creates a new conjunctive feature taking the package and name of the given classifier.
DiscreteReferrer(Classifier c, DiscreteFeature r) Sets both the identifier and the referent.
DiscreteReferrer(Classifier c, DiscreteFeature r, String[] av) Sets both the identifier and the referent.
DiscreteReferringFeature(Classifier c, ByteString i, DiscreteFeature r) Sets both the identifier and the referent.
DiscreteReferringFeature(Classifier c, ByteString i, DiscreteFeature r, String[] av) Sets both the identifier and the referent.
DiscreteReferringStringFeature(Classifier c, String i, DiscreteFeature r) Sets both the identifier and the referent.
DiscreteReferringStringFeature(Classifier c, String i, DiscreteFeature r, String[] av) Sets both the identifier and the referent.
MultiValueComparer(Classifier c, String v) Constructor.
RealConjunctiveFeature(Classifier c, Feature l, Feature r) Creates a new conjunctive feature taking the package and name of the given classifier.
RealReferrer(Classifier c, RealFeature r) Sets both the identifier and the referent.
RealReferringFeature(Classifier c, ByteString i, RealFeature r) Sets both the identifier and the referent.
RealReferringStringFeature(Classifier c, String i, RealFeature r) Sets both the identifier and the referent.
ValueComparer(Classifier c, String v) Constructor.

Uses of Classifier in edu.illinois.cs.cogcomp.lbjava.infer

Subclasses of Classifier in edu.illinois.cs.cogcomp.lbjava.infer

Modifier and Type	Class and Description
class	ParameterizedConstraint This class represents an LBJava constraint as it appears in a source file.

Uses of Classifier in edu.illinois.cs.cogcomp.lbjava.learn

Subclasses of Classifier in edu.illinois.cs.cogcomp.lbjava.learn

Modifier and Type	Class and Description
class	AdaBoost Implementation of the AdaBoost binary classification learning algorithm.
class	AdaGrad AdaGrad - Adaptive Stochastic Gradient Method AdaGrad alters the update to adapt based on historical information, so that frequent occurring features in the gradients get small learning rates and infrequent features get higher ones.
class	BinaryMIRA The Binary MIRA learning algorithm implementation.
class	Learner Extend this class to create a new Classifier that learns to mimic one an oracle classifier given a feature extracting classifier and example objects.
class	LinearThresholdUnit A LinearThresholdUnit is a Learner for binary classification in which a score is computed as a linear function a weight vector and the input example, and the decision is made by comparing the score to some threshold quantity.
class	MultiLabelLearner A simple implementation of a learner that learns from examples with multiple labels and is capable of predicting multiple labels on new examples.
class	MuxLearner A MuxLearner uses one of many Learners indexed by the first feature in an example to produce a classification.
class	NaiveBayes Naive Bayes is a multi-class learner that uses prediction value counts and feature counts given a particular prediction value to select the most likely prediction value.
class	PassiveAggressive The Passive Aggressive learning algorithm implementation.
class	SparseAveragedPerceptron An approximation to voted Perceptron, in which a weighted average of the weight vectors arrived at during training becomes the weight vector used to make predictions after training.
class	SparseConfidenceWeighted This is an implementation of the approximate "variance algorithm" of Confidence Weighted Linear Classification, Dredze, et.al (ICML, 2008).
class	SparseMIRA An implementation of the Margin Infused Relaxed Algorithm of Crammer and Singer.
class	SparseNetworkLearner A SparseNetworkLearner uses multiple LinearThresholdUnits to make a multi-class classification.
class	SparsePerceptron Simple sparse Perceptron implementation.
class	SparseWinnow Simple sparse Winnow implementation.
class	StochasticGradientDescent Gradient descent is a batch learning algorithm for function approximation in which the learner tries to follow the gradient of the error function to the solution of minimal error.
class	SupportVectorMachine Wrapper class for the liblinear library which supports support vector machine classification.
class	WekaWrapper Translates LBJava's internal problem representation into that which can be handled by WEKA learning algorithms.

Fields in edu.illinois.cs.cogcomp.lbjava.learn declared as Classifier

Modifier and Type	Field and Description
protected Classifier	Learner.extractor Stores the classifiers used to produce features.
protected Classifier	Learner.labeler Stores the classifier used to produce labels.

Methods in edu.illinois.cs.cogcomp.lbjava.learn that return Classifier

Modifier and Type	Method and Description
Classifier	Learner.getExtractor() Returns the extractor.
Classifier	Learner.getLabeler() Returns the labeler.

Methods in edu.illinois.cs.cogcomp.lbjava.learn with parameters of type Classifier

Modifier and Type	Method and Description
void	SparseNetworkLearner.setExtractor(Classifier e) Sets the extractor.
void	Learner.setExtractor(Classifier e) Sets the extractor.
void	SparseMIRA.setLabeler(Classifier l) Sets the labeler.
void	MuxLearner.setLabeler(Classifier l) Sets the labeler.
void	SupportVectorMachine.setLabeler(Classifier l) Sets the labels list.
void	SparseNetworkLearner.setLabeler(Classifier l) Sets the labeler.
void	NaiveBayes.setLabeler(Classifier l) Sets the labeler.
void	WekaWrapper.setLabeler(Classifier l) Sets the labeler.
void	LinearThresholdUnit.setLabeler(Classifier l) Sets the labels list.
void	Learner.setLabeler(Classifier l) Sets the labeler.
void	AdaBoost.setLabeler(Classifier l) Sets the labeler.
double	Accuracy.test(Classifier classifier, Classifier oracle, Parser parser) Evaluates a classifier against an oracle on the data provided by a parser.
double	TestingMetric.test(Classifier classifier, Classifier oracle, Parser parser) Evaluates a classifier against an oracle on the data provided by a parser.

Constructors in edu.illinois.cs.cogcomp.lbjava.learn with parameters of type Classifier

Constructor and Description
Learner(String n, Classifier e) Constructor for unsupervised learning.
Learner(String n, Classifier l, Classifier e) Constructor for supervised learning.

Uses of Classifier in edu.illinois.cs.cogcomp.lbjava.util

Methods in edu.illinois.cs.cogcomp.lbjava.util that return Classifier

Modifier and Type	Method and Description
static Classifier	ClassUtils.getClassifier(String name) Retrieve a Classifier by name using the no-argument constructor.
static Classifier	ClassUtils.getClassifier(String name, boolean exit) Retrieve a Classifier by name using the no-argument constructor.
static Classifier	ClassUtils.getClassifier(String name, Class[] paramTypes, Object[] arguments) Retrieve a Classifier by name using a constructor with arguments.
static Classifier	ClassUtils.getClassifier(String name, Class[] paramTypes, Object[] arguments, boolean exit) Retrieve a Classifier by name using a constructor with arguments.