Novel Algorithms for Noisy Minimization Problems with Applications to Neural Networks Training

Sirlantzis, Konstantinos and Lamb, John D. and Liu, Steve Wenbin (2006) Novel Algorithms for Noisy Minimization Problems with Applications to Neural Networks Training. Journal of Optimization Theory and Applications, 129 (2). pp. 325-340. ISSN 0022-3239. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1007/s10957-006-9066-z

Abstract

The supervisor and searcher cooperation framework (SSC), introduced in Refs. 1 and 2, provides an effective way to design efficient optimization algorithms combining the desirable features of the two existing ones. This work aims to develop efficient algorithms for a wide range of noisy optimization problems including those posed by feedforward neural networks training. It introduces two basic SSC algorithms. The first seems suited for generic problems. The second is motivated by neural networks training problems. It introduces also inexact variants of the two algorithms, which seem to possess desirable properties. It establishes general theoretical results about the convergence and speed of SSC algorithms and illustrates their appealing attributes through numerical tests on deterministic, stochastic, and neural networks training problems.

Item Type: Article
Subjects: T Technology
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: J. Harries
Date Deposited: 19 Dec 2007 18:16
Last Modified: 11 Jul 2014 13:06
Resource URI: http://kar.kent.ac.uk/id/eprint/481 (The current URI for this page, for reference purposes)
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