Package edu.illinois.cs.cogcomp.lbjava.learn

Interface Summary

Interface	Description
BatchTrainer.DoneWithRound	Provides access to a hook into BatchTrainer.train(int) so that additional processing can be performed at the end of each round.
TestingMetric	TestingMetric is an interface through which the user may implement their own testing method for use by LBJava's internal cross validation algorithm.

Class Summary

Class	Description
Accuracy	Returns the accuracy of a discrete classifier with respect to the oracle as the fraction of examples for which its prediction was correct.
AdaBoost	Implementation of the AdaBoost binary classification learning algorithm.
AdaBoost.Parameters	A container for all of AdaBoost's configurable parameters.
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.
AdaGrad.Parameters	A container for all of AdaGrad's configurable parameters.
BatchTrainer	Use this class to batch train a Learner.
BiasedRandomWeightVector	Same as the RandomWeightVector class that it extends, except that this vector also contains a bias term (also initialized randomly) which is added to every dot product and affected by every vector addition operation.
BiasedWeightVector	Same as the SparseWeightVector class that it extends, except that this vector also contains a bias term which is added to every dot product and affected by every vector addition operation.
BinaryMIRA	The Binary MIRA learning algorithm implementation.
BinaryMIRA.Parameters	Simply a container for all of BinaryMIRA's configurable parameters.
ChildLexicon	Instances of this class are intended to store features that are children of other features and which do not correspond to their own weights in any learner's weight vector.
IdentityNormalizer	This Normalizer simply returns the same ScoreSet it was passed as input without modifying anything.
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.
Learner.Parameters	Parameters classes are used to hold values for learning algorithm parameters, and all learning algorithm implementations must provide a constructor that takes such an object as input.
LearnerToText	This extremely simple class can be used to print a textual representation of a trained learner to STDOUT.
Lexicon	A Lexicon contains a mapping from Features to integers.
Lexicon.CountPolicy	Immutable type representing the feature counting policy of a lexicon.
Lexicon.PruningPolicy	Represents the feature counting policy of a lexicon.
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.
LinearThresholdUnit.Parameters	Simply a container for all of LinearThresholdUnit's configurable parameters.
Log	Simply turns each score s in the ScoreSet returned by the specified Normalizer into log(s).
MultiLabelLearner	A simple implementation of a learner that learns from examples with multiple labels and is capable of predicting multiple labels on new examples.
MultiLabelLearner.Parameters	Simply a container for all of MultiLabelLearner's configurable parameters.
MuxLearner	A MuxLearner uses one of many Learners indexed by the first feature in an example to produce a classification.
MuxLearner.Parameters	Simply a container for all of MuxLearner's configurable parameters.
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.
NaiveBayes.Count	A Count object stores two doubles, one which holds a accumulated count value and the other intended to hold the natural logarithm of the count.
NaiveBayes.Parameters	Simply a container for all of NaiveBayes's configurable parameters.
Normalizer	A normalizer is a function of a ScoreSet producing normalized scores.
PassiveAggressive	The Passive Aggressive learning algorithm implementation.
PassiveAggressive.Parameters	Simply a container for all of PassiveAggressive's configurable parameters.
RandomWeightVector	This weight vector operates similarly to its parent in the class hierarchy, but it halucinates (and sets) random values for weights corresponding to features it has never been asked about before.
Sigmoid	The sigmoid normalization function replaces each score xi with 1 / (1 + exp(-alpha xi)), where alpha is a user-specified constant.
Softmax	The softmax normalization function replaces each score with the fraction of its exponential out of the sum of all scores' exponentials.
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.
SparseAveragedPerceptron.AveragedWeightVector	This implementation of a sparse weight vector associates two doubles with each Feature.
SparseAveragedPerceptron.Parameters	Simply a container for all of SparseAveragedPerceptron's configurable parameters.
SparseConfidenceWeighted	This is an implementation of the approximate "variance algorithm" of Confidence Weighted Linear Classification, Dredze, et.al (ICML, 2008).
SparseConfidenceWeighted.Parameters	Simply a container for all of SparseConfidenceWeighted's configurable parameters.
SparseMIRA	An implementation of the Margin Infused Relaxed Algorithm of Crammer and Singer.
SparseMIRA.Parameters	Simply a container for all of SparseMIRA's configurable parameters.
SparseNetworkLearner	A SparseNetworkLearner uses multiple LinearThresholdUnits to make a multi-class classification.
SparseNetworkLearner.Parameters	Simply a container for all of SparseNetworkLearner's configurable parameters.
SparsePerceptron	Simple sparse Perceptron implementation.
SparsePerceptron.Parameters	Simply a container for all of SparsePerceptron's configurable parameters.
SparseWeightVector	This class is used as a weight vector in sparse learning algorithms.
SparseWinnow	Simple sparse Winnow implementation.
SparseWinnow.Parameters	Simply a container for all of SparseWinnow's configurable parameters.
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.
StochasticGradientDescent.Parameters	Simply a container for all of StochasticGradientDescent's configurable parameters.
SupportVectorMachine	Wrapper class for the liblinear library which supports support vector machine classification.
SupportVectorMachine.Parameters	A container for all of SupportVectorMachine's configurable parameters.
WekaWrapper	Translates LBJava's internal problem representation into that which can be handled by WEKA learning algorithms.
WekaWrapper.Parameters	Simply a container for all of WekaWrapper's configurable parameters.