Uses of Package
jsat.classifiers.neuralnetwork

Packages that use jsat.classifiers.neuralnetwork

Package	Description
jsat.classifiers.neuralnetwork

Classes in jsat.classifiers.neuralnetwork used by jsat.classifiers.neuralnetwork

Class and 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.
BackPropagationNet.WeightInitialization Different methods of initializing the weight values before training
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.
LVQ.LVQVersion There are several LVQ versions, each one adding an additional case in which two LVs instead of one can be updated.
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.
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.
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.