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 | 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 | |
ColumnDataClassifier |
ColumnDataClassifier provides a command-line interface for doing
context-free (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 tab-separated columns.
|
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 (
SVMLightClassifier s). |
WeightedDataset<L,F> |
Enum | Description |
---|---|
LogPrior.LogPriorType |