A supervised learning algorithm which uses data with associated target output to train an ANN.
An algorithm for efficiently calculating the error gradient of a neural network, which can then be used as the basis of learning.
The most common method of training an artificial neural network. A training set, consisting of examples of input data for which the output is known, is presented to the network, and the network weights are adjusted until the network produces results that are in agreement with the training set. This type of network is often used in prediction and in classification.
A multilayer feedforward neural net architecture which uses the supervised mode of learning. This is the most widely used type of neural net.
A training method used to calculate the weights in a neural net from the data.
Backpropagation is a supervised learning technique used for training artificial neural networks. It was first described by Paul Werbos in 1974, and further developed by David E. Rumelhart, Geoffrey E.