By Topic

Backpropagation for linearly-separable patterns: A detailed analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Frasconi, P. ; Dipartimento di Sistemi e Inf., Florence Univ., Italy ; Gori, M. ; Tesi, A.

A sufficient condition for learning without local minima in multilayered networks is proposed. A fundamental assumption on the network architecture is removed. It is proved that the conclusions drawn by M. Gori and A. Tesi (IEEE Trans. Pattern Anal. Mach. Intell., vol.14, no.1, pp.76-86, (1992)) also hold provided that the weight matrix associated with the hidden and output layer is pyramidal and has full rank. The analysis is carried out by using least mean squares (LMS)-threshold cost functions, which allow the identification of spurious and structural local minima

Published in:

Neural Networks, 1993., IEEE International Conference on

Date of Conference:

1993