Uses of Class
LBJ2.classify.Classifier

Packages that use Classifier

LBJ2.classify	Contains classes representing classifiers and features, as well as utility classes related to classifiers and features that may come in handy.
LBJ2.infer	Inference algorithms are implemented here (derived from Inference), but most of the classes in this package are used internally by LBJ at runtime to represent constraints and to translate between constraint representations.
LBJ2.learn	Learning algorithms, normalizers (used in inference; see Normalizer), testing metrics (used in cross validation; see TestingMetric), and other utility classes can be found in this package.
LBJ2.nlp	Parsers, data structures, pre-processing algorithms, and common feature extracting classifiers (implemented with LBJ) useful for natural language processing are implemented in this package.
LBJ2.nlp.seg	The segmentation of sequences of words into semantically meaningful groups is a common NLP paradigm; this package aims to support such tasks in a general way.
LBJ2.util	Utility routines for math related stuff, formatting, etc., are defined here.

Uses of Classifier in LBJ2.classify

Subclasses of Classifier in LBJ2.classify

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 LBJ2.classify declared as Classifier

private static Classifier	TestDiscrete.classifier References the classifier that is to be tested.
protected Classifier	ValueComparer.labeler The classifier whose value will be compared.
private static Classifier	TestDiscrete.oracle References the oracle classifier to test against.

Methods in LBJ2.classify that return Classifier

static Classifier	Classifier.binaryRead(java.io.ObjectInputStream ois, java.lang.String name) Reads in a binary representation of a Classifier from the specified stream as written by the binaryWrite(String) method.
static Classifier	Classifier.binaryRead(java.lang.String fileName) Reads in a binary representation of a Classifier from the specified file as written by the binaryWrite(String) method.
static Classifier	Classifier.binaryRead(java.lang.String fileName, java.lang.String name) Reads in a binary representation of a Classifier from the specified file as written by the binaryWrite(String) method.
static Classifier	Classifier.binaryRead(java.net.URL url, java.lang.String name) Reads in a binary representation of a Classifier from the specified URL as written by the binaryWrite(String) method.

Methods in LBJ2.classify with parameters of type Classifier

Feature	DiscreteFeature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
Feature	DiscreteArrayFeature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
Feature	RealFeature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
Feature	RealArrayFeature.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.
protected Feature	DiscreteFeature.conjunctWith(DiscreteArrayFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	DiscreteArrayFeature.conjunctWith(DiscreteArrayFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	RealFeature.conjunctWith(DiscreteArrayFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	RealArrayFeature.conjunctWith(DiscreteArrayFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected abstract Feature	Feature.conjunctWith(DiscreteArrayFeature 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	DiscreteArrayFeature.conjunctWith(DiscreteFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	RealFeature.conjunctWith(DiscreteFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	RealArrayFeature.conjunctWith(DiscreteFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected abstract 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(RealArrayFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	DiscreteArrayFeature.conjunctWith(RealArrayFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	RealFeature.conjunctWith(RealArrayFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	RealArrayFeature.conjunctWith(RealArrayFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected abstract Feature	Feature.conjunctWith(RealArrayFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	DiscreteFeature.conjunctWith(RealFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	DiscreteArrayFeature.conjunctWith(RealFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	RealFeature.conjunctWith(RealFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	RealArrayFeature.conjunctWith(RealFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected abstract 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, java.lang.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.

Constructors in LBJ2.classify with parameters of type Classifier

MultiValueComparer(Classifier c, java.lang.String v)
Constructor.

ValueComparer(Classifier c, java.lang.String v)
Constructor.

Uses of Classifier in LBJ2.infer

Subclasses of Classifier in LBJ2.infer

class

ParameterizedConstraint This class represents an LBJ constraint as it appears in a source file.

Uses of Classifier in LBJ2.learn

Subclasses of Classifier in LBJ2.learn

class	AdaBoost Implementation of the AdaBoost binary classification learning algorithm.
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	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	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	WekaWrapper Translates LBJ's internal problem representation into that which can be handled by WEKA learning algorithms.

Fields in LBJ2.learn declared as Classifier

protected Classifier	Learner.extractor Stores the classifiers used to produce features.
protected Classifier	Learner.labeler Stores the classifier used to produce labels.
protected Classifier	MuxLearner.select If this classifier's feature extractor is a composite generator, this member variable will reference the first child classifier of that composite generator; otherwise, it is null.

Methods in LBJ2.learn that return Classifier

Classifier	WekaWrapper.getExtractor() Returns the extractor.
Classifier	Learner.getExtractor() Returns the extractor.
Classifier	WekaWrapper.getLabeler() Returns the labeler.
Classifier	Learner.getLabeler() Returns the labeler.

Methods in LBJ2.learn with parameters of type Classifier

void	WekaWrapper.setExtractor(Classifier e) Sets the extractor.
void	MuxLearner.setExtractor(Classifier e) Sets the extractor.
void	SparseNetworkLearner.setExtractor(Classifier e) Sets the extractor.
void	AdaBoost.setExtractor(Classifier e) Sets the extractor.
void	Learner.setExtractor(Classifier e) Sets the extractor.
void	WekaWrapper.setLabeler(Classifier l) Sets the labeler.
void	MuxLearner.setLabeler(Classifier l) Sets the labeler.
void	SparseNetworkLearner.setLabeler(Classifier l) Sets the labeler.
void	AdaBoost.setLabeler(Classifier l) Sets the labeler.
void	Learner.setLabeler(Classifier l) Sets the labeler.
void	NaiveBayes.setLabeler(Classifier l) Sets the labeler.
void	LinearThresholdUnit.setLabeler(Classifier l) Sets the labels list.
double	TestingMetric.test(Classifier classifier, Classifier oracle, Parser parser) test is the function which LBJ's cross validation method will call in order to test an example.
double	Accuracy.test(Classifier classifier, Classifier oracle, Parser parser) The test method is what LBJ calls during its testing stage.

Constructors in LBJ2.learn with parameters of type Classifier

Learner(java.lang.String n, Classifier e)
Constructor for unsupervised learning.

Learner(java.lang.String n, Classifier l, Classifier e)
Constructor for supervised learning.

Uses of Classifier in LBJ2.nlp

Subclasses of Classifier in LBJ2.nlp

class	Affixes This class implements a classifier that takes a Word as input and generates features representing the prefixes and suffixes of the input word.
class	Capitalization This class implements a classifier that takes a Word as input and generates Boolean features representing the capitalizations of the words in a [-2, +2] window around the input word.
class	Forms This class implements a classifier that takes a Word as input and generates features representing the forms of the words in a [-2, +2] window around the input word.
class	WordTypeInformation This class implements a classifier that takes a Word as input and generates Boolean features representing interesting information about the forms of the words in a [-2, +2] window around the input word.

Uses of Classifier in LBJ2.nlp.seg

Fields in LBJ2.nlp.seg declared as Classifier

protected Classifier	BIOTester.classifier A BIO classifier that classifies Tokens.
protected Classifier	BIOTester.labeler A BIO classifier that produces the true labels of the Tokens.

Constructors in LBJ2.nlp.seg with parameters of type Classifier

BIOTester(Classifier c, Classifier l, Parser p)
Initializing constructor.

Uses of Classifier in LBJ2.util

Methods in LBJ2.util that return Classifier

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