| Class and Description |
|---|
| BatchTrainer
Use this class to batch train a
Learner. |
| 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. |
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 and Description |
|---|
| 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.
|
| Lexicon
A
Lexicon contains a mapping from Features to integers. |
| Class and Description |
|---|
| 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. |
| Normalizer
A normalizer is a function of a
ScoreSet producing normalized scores. |
| Class and Description |
|---|
| AdaBoost.Parameters
A container for all of
AdaBoost's configurable parameters. |
| AdaGrad.Parameters
A container for all of
AdaGrad's configurable parameters. |
| BatchTrainer.DoneWithRound
Provides access to a hook into
BatchTrainer.train(int) so that
additional processing can be performed at the end of each round. |
| 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.
|
| 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. |
| 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. |
| MultiLabelLearner.Parameters
Simply a container for all of
MultiLabelLearner's configurable parameters. |
| MuxLearner.Parameters
Simply a container for all of
MuxLearner's configurable parameters. |
| 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.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.
|
| 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.Parameters
Simply a container for all of
SparseConfidenceWeighted's configurable parameters. |
| 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.Parameters
Simply a container for all of
SparseWinnow's configurable parameters. |
| StochasticGradientDescent.Parameters
Simply a container for all of
StochasticGradientDescent's configurable parameters. |
| SupportVectorMachine.Parameters
A container for all of
SupportVectorMachine's configurable parameters. |
TestingMetric
TestingMetric is an interface through which the user may implement their own testing
method for use by LBJava's internal cross validation algorithm. |
| WekaWrapper.Parameters
Simply a container for all of
WekaWrapper's configurable parameters. |
| Class and Description |
|---|
| 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. |
Copyright © 2016. All rights reserved.