Classifies patterns into two classes.

Two-stage training using randomly selected training patterns in stage I.

A representation of a feed forward neural network.

Hidden layer in a neural network. This is created by a factory method in Network.

Input layer in a neural network.

A Node in the InputLayer.

The base class for Layers in a neural network.

Trains a FeedForwardNetwork using a Levenberg-Marquardt algorithm for minimizing a sum of squares error.

A link in a neural network.

Classifies patterns into three or more classes.

Neural network base class.

A Node in a neural network.

Output layer in a neural network.

A Perceptron in the OutputLayer.

A Perceptron node in a neural network.

Trains a Network using the quasi-Newton method, MinUnconMultiVar.

Scales or unscales continuous data prior to its use in neural network training, testing, or forecasting.

Converts time series data contained within nominal categories to a lagged format for processing by a neural network. Lagging is done within the nominal categories associated with the time series.

Converts time series data to a lagged format used as input to a neural network.

Converts nominal data into a series of binary encoded columns for input to a neural network. It also reverses the aforementioned encoding, accepting binary encoded data and returns an array of integers representing the classes for a nominal variable.

Encodes ordinal data into percentages for input to a neural network. It also allows decoding, accepting a percentage and converting it into an ordinal value.

Interface implemented by perceptron activation functions.

Interface implemented by classes used to train an Network.

Error function to be minimized by trainer.

Scaling Method

Transform type