Package jsat.classifiers.neuralnetwork

Class Summary

Class	Description
BackPropagationNet	An implementation of a Feed Forward Neural Network (NN) trained by Back Propagation.
BackPropagationNet.ActivationFunction	The neural network needs an activation function for the neurons that is used to predict from inputs and train the network by propagating the errors back through the network.
DReDNetSimple	This class provides a neural network based on Geoffrey Hinton's Deep Rectified Dropout Nets.
LVQ	Learning Vector Quantization (LVQ) is an algorithm that extends SOM to take advantage of label information to perform classification.
LVQLLC	LVQ with Locally Learned Classifier (LVQ-LLC) is an adaption of the LVQ algorithm I have come up with.
Perceptron	The perceptron is a simple algorithm that attempts to find a hyperplane that separates two classes.
RBFNet	This provides a highly configurable implementation of a Radial Basis Function Neural Network.
SGDNetworkTrainer	This class provides a highly configurable and generalized method of training a neural network using Stochastic Gradient Decent. Note, the API of this class may change in the future.
SOM	An implementation of a Self Organizing Map, also called a Kohonen Map.

Enum Summary

Enum	Description
BackPropagationNet.WeightInitialization	Different methods of initializing the weight values before training
LVQ.LVQVersion	There are several LVQ versions, each one adding an additional case in which two LVs instead of one can be updated.
RBFNet.Phase1Learner	The first phase of learning a RBF Neural Network is to determine the neuron locations.
RBFNet.Phase2Learner	The second phase of learning a RBF Neural Network is to determine how the neurons are activated to produce the output of the hidden layer.