Uses of Interface
jsat.classifiers.UpdateableClassifier

Packages that use UpdateableClassifier

Package	Description
jsat.classifiers
jsat.classifiers.bayesian
jsat.classifiers.boosting
jsat.classifiers.linear
jsat.classifiers.linear.kernelized
jsat.classifiers.svm.extended

Uses of UpdateableClassifier in jsat.classifiers

Classes in jsat.classifiers that implement UpdateableClassifier

Modifier and Type	Class and Description
class	BaseUpdateableClassifier A base implementation of the UpdateableClassifier.

Methods in jsat.classifiers that return UpdateableClassifier

Modifier and Type	Method and Description
UpdateableClassifier	UpdateableClassifier.clone()
abstract UpdateableClassifier	BaseUpdateableClassifier.clone()

Methods in jsat.classifiers with parameters of type UpdateableClassifier

Modifier and Type	Method and Description
static void	BaseUpdateableClassifier.trainEpochs(ClassificationDataSet dataSet, UpdateableClassifier toTrain, int epochs) Performs training on an updateable classifier by going over the whole data set in random order one observation at a time, multiple times.

Uses of UpdateableClassifier in jsat.classifiers.bayesian

Classes in jsat.classifiers.bayesian that implement UpdateableClassifier

Modifier and Type	Class and Description
class	AODE Averaged One-Dependence Estimators (AODE) is an extension of Naive Bayes that attempts to be more accurate by reducing the independence assumption.
class	MultinomialNaiveBayes An implementation of the Multinomial Naive Bayes model (MNB).
class	NaiveBayesUpdateable An implementation of Gaussian Naive Bayes that can be updated in an online fashion.
class	ODE One-Dependence Estimators (ODE) is an extension of Naive Bayes that, instead of assuming all features are independent, assumes all features are dependent on one other feature besides the target class.

Uses of UpdateableClassifier in jsat.classifiers.boosting

Classes in jsat.classifiers.boosting that implement UpdateableClassifier

Modifier and Type	Class and Description
class	UpdatableStacking This provides an implementation of the Stacking ensemble method meant for Updatable models.

Constructors in jsat.classifiers.boosting with parameters of type UpdateableClassifier

Constructor and Description
UpdatableStacking(UpdateableClassifier aggregatingClassifier, List<UpdateableClassifier> baseClassifiers) Creates a new Stacking classifier
UpdatableStacking(UpdateableClassifier aggregatingClassifier, UpdateableClassifier... baseClassifiers) Creates a new Stacking classifier.
UpdatableStacking(UpdateableClassifier aggregatingClassifier, UpdateableClassifier... baseClassifiers) Creates a new Stacking classifier.

Constructor parameters in jsat.classifiers.boosting with type arguments of type UpdateableClassifier

Constructor and Description
UpdatableStacking(UpdateableClassifier aggregatingClassifier, List<UpdateableClassifier> baseClassifiers) Creates a new Stacking classifier

Uses of UpdateableClassifier in jsat.classifiers.linear

Classes in jsat.classifiers.linear that implement UpdateableClassifier

Modifier and Type	Class and Description
class	ALMA2 Provides a linear implementation of the ALMAp algorithm for p = 2, which is considerably more efficient to compute.
class	AROW An implementation of Adaptive Regularization of Weight Vectors (AROW), which uses second order information to learn a large margin binary classifier.
class	LinearSGD LinearSGD learns either a classification or regression problem depending on the loss function ℓ(w,x) used.
class	NHERD Implementation of the Normal Herd (NHERD) algorithm for learning a linear binary classifier.
class	PassiveAggressive An implementations of the 3 versions of the Passive Aggressive algorithm for binary classification and regression.
class	ROMMA Provides an implementation of the linear Relaxed online Maximum Margin algorithm, which finds a similar solution to SVMs.
class	SCW Provides an Implementation of Confidence-Weighted (CW) learning and Soft Confidence-Weighted (SCW), both of which are binary linear classifiers inspired by PassiveAggressive.
class	SPA Support class Passive Aggressive (SPA) is a multi class generalization of PassiveAggressive.
class	STGD This provides an implementation of Sparse Truncated Gradient Descent for L1 regularized linear classification and regression on sparse data sets.

Uses of UpdateableClassifier in jsat.classifiers.linear.kernelized

Classes in jsat.classifiers.linear.kernelized that implement UpdateableClassifier

Modifier and Type	Class and Description
class	ALMA2K Provides a kernelized version of the ALMA2 algorithm.
class	BOGD Bounded Online Gradient Descent (BOGD) is a kernel learning algorithm that uses a bounded number of support vectors.
class	CSKLR An implementation of Conservative Stochastic Kernel Logistic Regression.
class	DUOL Provides an implementation of Double Update Online Learning (DUOL) algorithm.
class	Forgetron Implementation of the first two Forgetron algorithms.
class	KernelSGD Kernel SGD is the kernelized counterpart to LinearSGD, and learns nonlinear functions via the kernel trick.
class	OSKL Online Sparse Kernel Learning by Sampling and Smooth Losses (OSKL) is an online algorithm for learning sparse kernelized solutions to binary classification problems.
class	Projectron An implementation of the Projectron and Projectrion++ algorithms.

Uses of UpdateableClassifier in jsat.classifiers.svm.extended

Classes in jsat.classifiers.svm.extended that implement UpdateableClassifier

Modifier and Type	Class and Description
class	AMM This is the batch variant of the Adaptive Multi-Hyperplane Machine (AMM) algorithm.
class	OnlineAMM This is the Online variant of the Adaptive Multi-Hyperplane Machine (AMM) algorithm.