Uses of Interface
jsat.lossfunctions.LossFunc

Packages that use LossFunc

Package	Description
jsat.classifiers.linear
jsat.classifiers.linear.kernelized
jsat.lossfunctions

Uses of LossFunc in jsat.classifiers.linear

Methods in jsat.classifiers.linear that return LossFunc

Modifier and Type	Method and Description
LossFunc	LinearSGD.getLoss() Returns the loss function in use
LossFunc	LinearBatch.getLoss() Returns the loss function in use

Methods in jsat.classifiers.linear with parameters of type LossFunc

Modifier and Type	Method and Description
void	LinearSGD.setLoss(LossFunc loss) Sets the loss function used for the model.
void	LinearBatch.setLoss(LossFunc loss) Sets the loss function used for the model.

Constructors in jsat.classifiers.linear with parameters of type LossFunc

Constructor and Description
GradFunction(DataSet D, LossFunc loss)
LinearBatch(LossFunc loss, double lambda0) Creates a new Linear Batch learner
LinearBatch(LossFunc loss, double lambda0, double tolerance) Creates a new Linear Batch learner
LinearBatch(LossFunc loss, double lambda0, double tolerance, Optimizer2 optimizer) Creates a new Linear Batch learner
LinearSGD(LossFunc loss, double eta, DecayRate decay, double lambda0, double lambda1) Creates a new LinearSGD learner.
LinearSGD(LossFunc loss, double lambda0, double lambda1) Creates a new LinearSGD learner
LossFunction(DataSet D, LossFunc loss)
SCD(LossFunc loss, double regularization, int iterations) Creates anew SCD learner

Uses of LossFunc in jsat.classifiers.linear.kernelized

Methods in jsat.classifiers.linear.kernelized that return LossFunc

Modifier and Type	Method and Description
LossFunc	KernelSGD.getLoss() Returns the loss function in use

Methods in jsat.classifiers.linear.kernelized with parameters of type LossFunc

Modifier and Type	Method and Description
void	KernelSGD.setLoss(LossFunc loss) Sets the loss function to use.

Constructors in jsat.classifiers.linear.kernelized with parameters of type LossFunc

Constructor and Description
KernelSGD(LossFunc loss, KernelTrick kernel, double lambda, KernelPoint.BudgetStrategy budgetStrategy, int budgetSize) Creates a new Kernel SGD object
KernelSGD(LossFunc loss, KernelTrick kernel, double lambda, KernelPoint.BudgetStrategy budgetStrategy, int budgetSize, double eta, double errorTolerance) Creates a new Kernel SGD object

Uses of LossFunc in jsat.lossfunctions

Subinterfaces of LossFunc in jsat.lossfunctions

Modifier and Type	Interface and Description
interface	LossC Specifies a loss function for binary classification problems.
interface	LossMC Specifies a loss function for multi-class problems.
interface	LossR Specifies a getLoss function for regression problems.

Classes in jsat.lossfunctions that implement LossFunc

Modifier and Type	Class and Description
class	AbsoluteLoss The AbsoluteLoss loss function for regression L(x, y) = |x-y|.
class	EpsilonInsensitiveLoss The ε-insensitive loss for regression L(x, y) = max(0, |x-y|-ε) is the common loss function used for Support Vector Regression.
class	HingeLoss The HingeLoss loss function for classification L(x, y) = max(0, 1-y*x) .
class	HuberLoss The HuberLoss loss function for regression.
class	LogisticLoss The LogisticLoss loss function for classification L(x, y) = log(1+exp(-y*x)).
class	SoftmaxLoss The Softmax loss function is a multi-class generalization of the Logistic loss.
class	SquaredLoss The SquaredLoss loss function for regression L(x, y) = (x-y)2.

Methods in jsat.lossfunctions that return LossFunc

Modifier and Type	Method and Description
LossFunc	LossFunc.clone()