Uses of Package
jsat.math.optimization.stochastic

Packages that use jsat.math.optimization.stochastic

Package	Description
jsat.classifiers.linear
jsat.classifiers.neuralnetwork
jsat.math.optimization.stochastic

Classes in jsat.math.optimization.stochastic used by jsat.classifiers.linear

Class and Description
GradientUpdater This interface defines the method of updating some weight vector using a gradient and a learning rate.

Classes in jsat.math.optimization.stochastic used by jsat.classifiers.neuralnetwork

Class and Description
GradientUpdater This interface defines the method of updating some weight vector using a gradient and a learning rate.

Classes in jsat.math.optimization.stochastic used by jsat.math.optimization.stochastic

Class and Description
AdaDelta AdaDelta is inspired by AdaGrad and was developed for use primarily in neural networks.
AdaGrad AdaGrad provides an adaptive learning rate for each individual feature See: Duchi, J., Hazan, E.,&Singer, Y.
Adam Adam is inspired by RMSProp and AdaGrad, where the former can be seen as a special case of Adam.
GradientUpdater This interface defines the method of updating some weight vector using a gradient and a learning rate.
NAdaGrad Normalized AdaGrad provides an adaptive learning rate for each individual feature, and is mostly scale invariant to the data distribution.
RMSProp rmsprop is an adpative learning weight scheme proposed by Geoffrey Hinton.
Rprop The Rprop algorithm provides adaptive learning rates using only first order information.
SGDMomentum Performs unaltered Stochastic Gradient Decent updates using either standard or Nestrov momentum.
SimpleSGD Performs unaltered Stochastic Gradient Decent updates computing x = x- η grad Because the SimpleSGD requires no internal state, it is not necessary to call SimpleSGD.setup(int).