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
realvalued features.

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  
ClassifierExample 
Sample code that illustrates the training and use of a linear classifier.

ColumnDataClassifier 
ColumnDataClassifier provides a commandline interface for doing
contextfree (independent) classification of a series of data items,
where each data item is represented by a line of
a file, as a list of String variables, in tabseparated columns.

CrossValidator<L,F> 
This class is meant to simplify performing cross validation of
classifiers for hyperparameters.

CrossValidator.SavedState  
Dataset<L,F> 
An interfacing class for
ClassifierFactory that incrementally
builds a more memoryefficient 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>  
LogConditionalEqConstraintFunction 
Maximizes the conditional likelihood with a given prior.

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.

NaiveBayesClassifier<L,F>  
NaiveBayesClassifierFactory<L,F> 
Creates a NaiveBayesClassifier given an RVFDataset.

NBLinearClassifierFactory<L,F> 
Provides a mediumweight implementation of Bernoulli (or binary)
Naive Bayes via a linear classifier.

NominalDataReader 
A class to read some UCI datasets into RVFDatum.

PRCurve 
A class to create recallprecision 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 memoryefficient 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 (
SVMLightClassifier s). 
WeightedDataset<L,F> 
Enum  Description 

LogPrior.LogPriorType 