Simultaneous design and training of ontogenic neural network classifiers

18Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The use of neural networks in pattern classification is a relatively recent phenomena. In some instances the nonparametric neural network approach has demonstrated significant advantages over more conventional methods. However, certain of the drawbacks of neural networks have led to interest in the augmentation of the neural network approach with such supporting tools as genetic algorithms (e.g. in support of neural network training). In this paper, we take yet a further step. Specifically, we present an approach for the simultaneous design and training of neural networks by means of a tailored genetic algorithm. We then demonstrate its employment on the problem of the classification of firms with regard to future fiscal well-being (i.e. are they likely to fail or survive). The resulting ontogenic neural network exhibits, we believe, some particularly attractive characteristics. Copyright © 1996 Elsevier Science Ltd.

Cite

CITATION STYLE

APA

Ignizio, J. P., & Soltys, J. R. (1996). Simultaneous design and training of ontogenic neural network classifiers. Computers and Operations Research, 23(6 SPEC. ISS.), 535–546. https://doi.org/10.1016/0305-0548(95)00058-5

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free