๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Generalization and learning error for nonlinear perceptron

โœ Scribed by M. Shcherbina; B. Tirozzi

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
763 KB
Volume
35
Category
Article
ISSN
0895-7177

No coin nor oath required. For personal study only.

โœฆ Synopsis

rigorous derivation of the asymptotic behaviour of learning and prediction error for the nonlinear perceptron is presented. The saddle-point method is used for evaluating these quantities.

๐Ÿ“œ SIMILAR VOLUMES

Geometric learning algorithm for element
Geometric learning algorithm for elementary perceptron and its convergence conditions
โœ Seiji Miyoshi; Kazushi Ikeda; Kenji Nakayama ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 258 KB

In this paper, the geometric learning algorithm (GLA) is proposed for an elementary perceptron which includes a single output neuron. The GLA is a modified version of the affine projection algorithm (APA) for adaptive filters. The weight update vector is determined geometrically with respect to the