Uses of Interface
gov.sandia.cognition.learning.algorithm.SupervisedIncrementalLearner

Packages that use SupervisedIncrementalLearner

Package	Description
gov.sandia.cognition.learning.algorithm	Provides general interfaces for learning algorithms.
gov.sandia.cognition.learning.algorithm.confidence	Provides confidence-weighted categorization algorithms.
gov.sandia.cognition.learning.algorithm.ensemble	Provides ensemble methods.
gov.sandia.cognition.learning.algorithm.perceptron	Provides the Perceptron algorithm and some of its variations.
gov.sandia.cognition.learning.algorithm.perceptron.kernel

Uses of SupervisedIncrementalLearner in gov.sandia.cognition.learning.algorithm

Subinterfaces of SupervisedIncrementalLearner in gov.sandia.cognition.learning.algorithm

Modifier and Type	Interface and Description
interface	SupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> Interface for a class that is a supervised learning algorithm that can be used both batch and incremental contexts.

Classes in gov.sandia.cognition.learning.algorithm that implement SupervisedIncrementalLearner

Modifier and Type	Class and Description
class	AbstractSupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> An abstract implementation of the batch and incremental learning for an incremental supervised learner.

Uses of SupervisedIncrementalLearner in gov.sandia.cognition.learning.algorithm.confidence

Classes in gov.sandia.cognition.learning.algorithm.confidence that implement SupervisedIncrementalLearner

Modifier and Type	Class and Description
class	AdaptiveRegularizationOfWeights An implementation of the Adaptive Regularization of Weights (AROW) algorithm for online learning of a linear binary categorizer.
class	ConfidenceWeightedDiagonalDeviation An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer.
class	ConfidenceWeightedDiagonalDeviationProject An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer.
class	ConfidenceWeightedDiagonalVariance An implementation of the Variance algorithm for learning a confidence-weighted linear categorizer.
class	ConfidenceWeightedDiagonalVarianceProject An implementation of the Variance algorithm for learning a confidence-weighted linear categorizer.

Uses of SupervisedIncrementalLearner in gov.sandia.cognition.learning.algorithm.ensemble

Classes in gov.sandia.cognition.learning.algorithm.ensemble that implement SupervisedIncrementalLearner

Modifier and Type	Class and Description
class	OnlineBaggingCategorizerLearner<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>> An implementation of an online version of the Bagging algorithm for learning an ensemble of categorizers.

Uses of SupervisedIncrementalLearner in gov.sandia.cognition.learning.algorithm.perceptron

Subinterfaces of SupervisedIncrementalLearner in gov.sandia.cognition.learning.algorithm.perceptron

Modifier and Type	Interface and Description
interface	KernelizableBinaryCategorizerOnlineLearner Interface for an online learner of a linear binary categorizer that can also be used with a kernel function.
interface	LinearizableBinaryCategorizerOnlineLearner<InputType> Interface for an online learner of a kernel binary categorizer that can also be used for learning a linear categorizer.

Classes in gov.sandia.cognition.learning.algorithm.perceptron that implement SupervisedIncrementalLearner

Modifier and Type	Class and Description
class	AbstractKernelizableBinaryCategorizerOnlineLearner An abstract implementation of the KernelizableBinaryCategorizerOnlineLearner interface.
class	AbstractLinearCombinationOnlineLearner An abstract class for online learning of linear binary categorizers that take the form of a weighted sum of inputs.
class	AbstractOnlineLinearBinaryCategorizerLearner An abstract class for online (incremental) learning algorithms that produce an LinearBinaryCategorizer.
class	AggressiveRelaxedOnlineMaximumMarginAlgorithm An implementation of the Aggressive Relaxed Online Maximum Margin Algorithm (AROMMA).
class	Ballseptron An implementation of the Ballseptron algorithm.
class	OnlineBinaryMarginInfusedRelaxedAlgorithm An implementation of the binary MIRA algorithm.
class	OnlinePassiveAggressivePerceptron An implementation of the Passive-Aggressive algorithm for learning a linear binary categorizer.
static class	OnlinePassiveAggressivePerceptron.AbstractSoftMargin An abstract class for soft-margin versions of the Passive-Aggressive algorithm.
static class	OnlinePassiveAggressivePerceptron.LinearSoftMargin An implementation of the linear soft-margin variant of the Passive- Aggressive algorithm (PA-I).
static class	OnlinePassiveAggressivePerceptron.QuadraticSoftMargin An implementation of the quadratic soft-margin variant of the Passive- Aggressive algorithm (PA-II).
class	OnlinePerceptron An online version of the classic Perceptron algorithm.
class	OnlineRampPassiveAggressivePerceptron An implementation of the Ramp Loss Passive Aggressive Perceptron (PA^R) from the referenced paper.
class	OnlineShiftingPerceptron An implementation of the Shifting Perceptron algorithm.
class	OnlineVotedPerceptron An online version of the Voted-Perceptron algorithm.
class	RelaxedOnlineMaximumMarginAlgorithm An implementation of the Relaxed Online Maximum Margin Algorithm (ROMMA).
class	Winnow An implementation of the Winnow incremental learning algorithm.

Methods in gov.sandia.cognition.learning.algorithm.perceptron that return SupervisedIncrementalLearner

Modifier and Type	Method and Description
SupervisedIncrementalLearner<Vectorizable,java.lang.Boolean,LinearBinaryCategorizer>	LinearizableBinaryCategorizerOnlineLearner.createLinearLearner(VectorFactory<?> vectorFactory) Creates a new linear learner using the standard learning interfaces based on this learner and its parameters.

Uses of SupervisedIncrementalLearner in gov.sandia.cognition.learning.algorithm.perceptron.kernel

Classes in gov.sandia.cognition.learning.algorithm.perceptron.kernel that implement SupervisedIncrementalLearner

Modifier and Type	Class and Description
class	AbstractOnlineBudgetedKernelBinaryCategorizerLearner<InputType> An abstract implementation of the BudgetedKernelBinaryCategorizerLearner for online learners.
class	AbstractOnlineKernelBinaryCategorizerLearner<InputType> An abstract class for an online kernel binary categorizer learner.
class	Forgetron<InputType> An implementation of the "self-tuned" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
static class	Forgetron.Basic<InputType> An implementation of the "basic" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
static class	Forgetron.Greedy<InputType> An implementation of the "greedy" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
class	KernelBinaryCategorizerOnlineLearnerAdapter<InputType> A wrapper class for a KernelizableBinaryCategorizerOnlineLearner that allows it to be used as a batch or incremental learner over the input type directly, rather than using utility methods.
class	OnlineKernelPerceptron<InputType> An implementation of the online version of the Perceptron algorithm.
class	OnlineKernelRandomizedBudgetPerceptron<InputType> An implementation of a fixed-memory kernel Perceptron algorithm.
class	Projectron<InputType> An implementation of the Projectron algorithm, which is an online kernel binary categorizer learner that has a budget parameter tuned by the eta parameter.
static class	Projectron.LinearSoftMargin<InputType> An implementation of the Projectron++ algorithm, which is an online kernel binary categorizer learner that has a budget parameter tuned by the eta parameter.
class	RemoveOldestKernelPerceptron<InputType> A budget kernel Perceptron that always removes the oldest item.
class	Stoptron<InputType> An online, budgeted, kernel version of the Perceptron algorithm that stops learning once it has reached its budget.