Uses of Interface
org.tribuo.multilabel.sgd.MultiLabelObjective
Package
Description
Provides an implementation of a multi-label classification factorization machine model using Stochastic Gradient Descent.
Provides an implementation of a multi-label classification linear model using Stochastic Gradient Descent.
Provides multi-label classification loss functions for Stochastic Gradient Descent.
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Uses of MultiLabelObjective in org.tribuo.multilabel.sgd.fm
Modifier and TypeMethodDescriptionFMMultiLabelOptions.getLoss()
Returns the loss function specified in the arguments.ModifierConstructorDescriptionFMMultiLabelTrainer
(MultiLabelObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, int minibatchSize, long seed, int factorizedDimSize, double variance) Constructs an SGD trainer for a multi-label factorization machine.FMMultiLabelTrainer
(MultiLabelObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, long seed, int factorizedDimSize, double variance) Constructs an SGD trainer for a multi-label factorization machine.FMMultiLabelTrainer
(MultiLabelObjective objective, StochasticGradientOptimiser optimiser, int epochs, long seed, int factorizedDimSize, double variance) Constructs an SGD trainer for a multi-label factorization machine. -
Uses of MultiLabelObjective in org.tribuo.multilabel.sgd.linear
Modifier and TypeMethodDescriptionLinearSGDOptions.getLoss()
Returns the loss function specified in the arguments.ModifierConstructorDescriptionLinearSGDTrainer
(MultiLabelObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, int minibatchSize, long seed) Constructs an SGD trainer for a linear model.LinearSGDTrainer
(MultiLabelObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, long seed) Constructs an SGD trainer for a linear model.LinearSGDTrainer
(MultiLabelObjective objective, StochasticGradientOptimiser optimiser, int epochs, long seed) Constructs an SGD trainer for a linear model. -
Uses of MultiLabelObjective in org.tribuo.multilabel.sgd.objectives
Modifier and TypeClassDescriptionfinal class
A multilabel version of binary cross entropy loss which expects logits.final class
Hinge loss, scores the correct value margin and any incorrect predictions -margin.