Quickprop

Quickprop is an iterative method for determining the minimum of the loss function of an artificial neural network, following an algorithm inspired by the Newton's method. Sometimes, the algorithm is classified to the group  of the second order learning methods. It follows a quadratic approximation of the previous gradient step and the current gradient, which is expected to be close to the minimum of the loss function, under the assumption that the loss function is locally approximately square, trying to describe it by means of an upwardly open parabola. The minimum is sought in the vertex of the parabola. The procedure requires only local information of the artificial neuron to which it is applied.
The -th approximation step is given by:

Being  the neuron  weight of its  input and  is the loss function.

The Quickprop algorithm is an implementation of the error backpropagation algorithm, but the network  can behave chaotically during the learning phase due to large step sizes.

Bibliography 

Scott E. Fahlman: An Empirical Study of Learning Speed in Back-Propagation Networks, September 1988

Category:Machine learning algorithms
Category:Artificial neural networks
Category:Computational neuroscience