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

Packages that use SupervisedIncrementalLearner
gov.sandia.cognition.learning.algorithm Provides general interfaces for learning algorithms. 
gov.sandia.cognition.learning.algorithm.confidence   
gov.sandia.cognition.learning.algorithm.ensemble Provides ensmble 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
 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
 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
 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
 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
 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
 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
 SupervisedIncrementalLearner<Vectorizable,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
 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.