Uses of Package
jsat.lossfunctions

Packages that use jsat.lossfunctions

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

Classes in jsat.lossfunctions used by jsat.classifiers.linear

Class and Description
LossFunc Provides a generic interface for some loss function on some problem that can be described with a single real prediction value and a single real expected value.
LossMC Specifies a loss function for multi-class problems.

Classes in jsat.lossfunctions used by jsat.classifiers.linear.kernelized

Class and Description
LossC Specifies a loss function for binary classification problems.
LossFunc Provides a generic interface for some loss function on some problem that can be described with a single real prediction value and a single real expected value.

Classes in jsat.lossfunctions used by jsat.lossfunctions

Class and Description
AbsoluteLoss The AbsoluteLoss loss function for regression L(x, y) = |x-y|.
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.
HingeLoss The HingeLoss loss function for classification L(x, y) = max(0, 1-y*x) .
HuberLoss The HuberLoss loss function for regression.
LogisticLoss The LogisticLoss loss function for classification L(x, y) = log(1+exp(-y*x)).
LossC Specifies a loss function for binary classification problems.
LossFunc Provides a generic interface for some loss function on some problem that can be described with a single real prediction value and a single real expected value.
LossMC Specifies a loss function for multi-class problems.
LossR Specifies a getLoss function for regression problems.
SquaredLoss The SquaredLoss loss function for regression L(x, y) = (x-y)2.