gov.sandia.cognition.learning.algorithm

Interface BatchAndIncrementalLearner<DataType,ResultType>

Type Parameters:

DataType - Type of the input data.

ResultType - Result type from both the online- and batch-learning interfaces.

All Superinterfaces:

BatchLearner<java.util.Collection<? extends DataType>,ResultType>, java.lang.Cloneable, CloneableSerializable, IncrementalLearner<DataType,ResultType>, java.io.Serializable

All Known Subinterfaces:

IncrementalEstimator<DataType,DistributionType,SufficientStatisticsType>, KernelizableBinaryCategorizerOnlineLearner, LinearizableBinaryCategorizerOnlineLearner<InputType>, SupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType>

All Known Implementing Classes:

AbstractBatchAndIncrementalLearner, AbstractConjugatePriorBayesianEstimator, AbstractIncrementalEstimator, AbstractKalmanFilter, AbstractKernelizableBinaryCategorizerOnlineLearner, AbstractLinearCombinationOnlineLearner, AbstractOnlineBudgetedKernelBinaryCategorizerLearner, AbstractOnlineKernelBinaryCategorizerLearner, AbstractOnlineLinearBinaryCategorizerLearner, AbstractParticleFilter, AbstractSupervisedBatchAndIncrementalLearner, AdaptiveRegularizationOfWeights, AggressiveRelaxedOnlineMaximumMarginAlgorithm, Ballseptron, BernoulliBayesianEstimator, BinomialBayesianEstimator, ConfidenceWeightedDiagonalDeviation, ConfidenceWeightedDiagonalDeviationProject, ConfidenceWeightedDiagonalVariance, ConfidenceWeightedDiagonalVarianceProject, DefaultDataDistribution.Estimator, DefaultDataDistribution.WeightedEstimator, ExponentialBayesianEstimator, ExtendedKalmanFilter, Forgetron, Forgetron.Basic, Forgetron.Greedy, GammaInverseScaleBayesianEstimator, KalmanFilter, KernelBinaryCategorizerOnlineLearnerAdapter, MultinomialBayesianEstimator, MultivariateGaussian.IncrementalEstimator, MultivariateGaussian.IncrementalEstimatorCovarianceInverse, MultivariateGaussianMeanBayesianEstimator, MultivariateGaussianMeanCovarianceBayesianEstimator, OnlineBaggingCategorizerLearner, OnlineBinaryMarginInfusedRelaxedAlgorithm, OnlineKernelPerceptron, OnlineKernelRandomizedBudgetPerceptron, OnlineMultiPerceptron, OnlineMultiPerceptron.ProportionalUpdate, OnlineMultiPerceptron.UniformUpdate, OnlinePassiveAggressivePerceptron, OnlinePassiveAggressivePerceptron.AbstractSoftMargin, OnlinePassiveAggressivePerceptron.LinearSoftMargin, OnlinePassiveAggressivePerceptron.QuadraticSoftMargin, OnlinePerceptron, OnlineRampPassiveAggressivePerceptron, OnlineShiftingPerceptron, OnlineVotedPerceptron, PoissonBayesianEstimator, Projectron, Projectron.LinearSoftMargin, RelaxedOnlineMaximumMarginAlgorithm, RemoveOldestKernelPerceptron, SamplingImportanceResamplingParticleFilter, ScalarDataDistribution.Estimator, SimpleStatisticalSpellingCorrector.Learner, Stoptron, UniformDistributionBayesianEstimator, UnivariateGaussian.IncrementalEstimator, UnivariateGaussianMeanBayesianEstimator, UnivariateGaussianMeanVarianceBayesianEstimator, VectorNaiveBayesCategorizer.OnlineLearner, Winnow

public interface BatchAndIncrementalLearner<DataType,ResultType>
extends BatchLearner<java.util.Collection<? extends DataType>,ResultType>, IncrementalLearner<DataType,ResultType>

Interface for an algorithm that is both a batch and incremental learner.

Since:

3.2.0

Author:

Justin Basilico

Method Summary

Modifier and Type	Method and Description
ResultType	learn(java.lang.Iterable<? extends DataType> data) Creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

learn

Methods inherited from interface gov.sandia.cognition.learning.algorithm.IncrementalLearner

createInitialLearnedObject, update, update

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Method Detail

learn

ResultType learn(java.lang.Iterable<? extends DataType> data)

Creates an object of ResultType using data of type DataType, using some form of "learning" algorithm. Typically implemented as a convenience method for calling an incremental learner on each data point.

Parameters:

data - The data that the learning algorithm will use to create an object of ResultType.

Returns:

The object that is created based on the given data using the learning algorithm.