Almeida-Pineda recurrent backpropagation
Encyclopedia
Almeida–Pineda recurrent backpropagation is an extension to the backpropagation
algorithm
that is applicable to recurrent neural networks. It is a type of supervised learning
.
A recurrent neural network for this algorithm consists of some input units, some output units and eventually some hidden units.
For a given set of (input, target) states, the network is trained to settle into a stable activation state with the output units in the target state, based on a given input state clamped on the input units.
Backpropagation
Backpropagation is a common method of teaching artificial neural networks how to perform a given task. Arthur E. Bryson and Yu-Chi Ho described it as a multi-stage dynamic system optimization method in 1969 . It wasn't until 1974 and later, when applied in the context of neural networks and...
algorithm
Algorithm
In mathematics and computer science, an algorithm is an effective method expressed as a finite list of well-defined instructions for calculating a function. Algorithms are used for calculation, data processing, and automated reasoning...
that is applicable to recurrent neural networks. It is a type of supervised learning
Supervised learning
Supervised learning is the machine learning task of inferring a function from supervised training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object and a desired output value...
.
A recurrent neural network for this algorithm consists of some input units, some output units and eventually some hidden units.
For a given set of (input, target) states, the network is trained to settle into a stable activation state with the output units in the target state, based on a given input state clamped on the input units.