- Type Parameters:
InputType - The type of input data in the input-output pair that
the learner can learn from. The Evaluator learned from the
algorithm also takes this as the input parameter.OutputType - The type of output data in the input-output pair that
the learner can learn from. The Evaluator learned from the
algorithm also produces this as its output.ResultType - The type of object created by the learning algorithm.
For example, a FeedforwardNeuralNetwork.
- All Superinterfaces:
- BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,ResultType>, java.lang.Cloneable, CloneableSerializable, java.io.Serializable
- All Known Subinterfaces:
- KernelizableBinaryCategorizerOnlineLearner, LinearizableBinaryCategorizerOnlineLearner<InputType>, MultivariateRegression<InputType,EvaluatorType>, ParameterCostMinimizer<ResultType>, Regression<InputType,OutputType,EvaluatorType>, SupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType>, UnivariateRegression<InputType,EvaluatorType>
- All Known Implementing Classes:
- AbstractAnytimeSupervisedBatchLearner, AbstractBaggingLearner, AbstractDeltaCategorizer.AbstractLearner, AbstractFactorizationMachineLearner, AbstractKernelizableBinaryCategorizerOnlineLearner, AbstractLinearCombinationOnlineLearner, AbstractLogisticRegression, AbstractMinimizerBasedParameterCostMinimizer, AbstractOnlineBudgetedKernelBinaryCategorizerLearner, AbstractOnlineKernelBinaryCategorizerLearner, AbstractOnlineLinearBinaryCategorizerLearner, AbstractParameterCostMinimizer, AbstractSupervisedBatchAndIncrementalLearner, AdaBoost, AdaptiveRegularizationOfWeights, AggressiveRelaxedOnlineMaximumMarginAlgorithm, BaggingCategorizerLearner, BaggingRegressionLearner, Ballseptron, BatchMultiPerceptron, BinaryBaggingLearner, BinaryCategorizerSelector, BinaryVersusCategorizer.Learner, BurrowsDeltaCategorizer.Learner, CategorizationTreeLearner, CategoryBalancedBaggingLearner, CategoryBalancedIVotingLearner, ConfidenceWeightedDiagonalDeviation, ConfidenceWeightedDiagonalDeviationProject, ConfidenceWeightedDiagonalVariance, ConfidenceWeightedDiagonalVarianceProject, CosineDeltaCategorizer.Learner, DiscreteNaiveBayesCategorizer.Learner, EvaluatorToCategorizerAdapter.Learner, FactorizationMachineAlternatingLeastSquares, FactorizationMachineStochasticGradient, FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver, FletcherXuHybridEstimation, Forgetron, Forgetron.Basic, Forgetron.Greedy, GaussNewtonAlgorithm, InputOutputTransformedBatchLearner, IVotingCategorizerLearner, KernelAdatron, KernelBasedIterativeRegression, KernelBinaryCategorizerOnlineLearnerAdapter, KernelPerceptron, KernelWeightedRobustRegression, KNearestNeighborExhaustive.Learner, KNearestNeighborKDTree.Learner, LeastSquaresEstimator, LevenbergMarquardtEstimation, LinearBasisRegression, LinearRegression, LocallyWeightedFunction.Learner, LogisticRegression, MaximumAPosterioriCategorizer.Learner, MeanLearner, MostFrequentLearner, MultiCategoryAdaBoost, MultivariateLinearRegression, NearestNeighborExhaustive.Learner, NearestNeighborKDTree.Learner, OnlineBaggingCategorizerLearner, OnlineBinaryMarginInfusedRelaxedAlgorithm, OnlineKernelPerceptron, OnlineKernelRandomizedBudgetPerceptron, OnlinePassiveAggressivePerceptron, OnlinePassiveAggressivePerceptron.AbstractSoftMargin, OnlinePassiveAggressivePerceptron.LinearSoftMargin, OnlinePassiveAggressivePerceptron.QuadraticSoftMargin, OnlinePerceptron, OnlineRampPassiveAggressivePerceptron, OnlineShiftingPerceptron, OnlineVotedPerceptron, ParameterDerivativeFreeCostMinimizer, ParameterDifferentiableCostMinimizer, Perceptron, PolynomialFunction.Regression, PrimalEstimatedSubGradient, Projectron, Projectron.LinearSoftMargin, RegressionTreeLearner, RelaxedOnlineMaximumMarginAlgorithm, RemoveOldestKernelPerceptron, SequentialMinimalOptimization, Stoptron, SuccessiveOverrelaxation, TimeSeriesPredictionLearner, UnivariateLinearRegression, VectorFunctionToScalarFunction.Learner, VectorNaiveBayesCategorizer.BatchGaussianLearner, VectorNaiveBayesCategorizer.Learner, WeightedMeanLearner, WeightedMostFrequentLearner, WinnerTakeAllCategorizer.Learner, Winnow
@CodeReview(reviewer="Kevin R. Dixon",
date="2008-07-22",
changesNeeded=false,
comments="Interface looks fine.")
public interface SupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
extends BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,ResultType>
The BatchSupervisedLearner interface is an extension of the
BatchLearner interface that contains the typical generic definition
conventions for a batch, supervised learning algorithm. That is, it takes
a collection of input-output pairs and learns an evaluator that can take the
the input value type and return the output value type. The interface does
not define any extra functionality, it just provides a convenience for the
large generic parameter definition.
- Since:
- 2.1
- Author:
- Justin Basilico, Kevin R. Dixon