Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10323086 | Expert Systems with Applications | 2005 | 16 Pages |
Abstract
In this study, two learning paradigms of neural networks, supervised versus unsupervised, are compared using their representative types. The back-propagation (BP) network and the Kohonen self-organizing feature map, selected as the representative type for supervised and unsupervised neural networks, respectively, are compared in terms of prediction accuracy in the area of bankruptcy prediction. Discriminant analysis and logistic regression are also performed to provide performance benchmarks. The findings suggest that the BP network is a better choice when a target vector is available.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Kidong Lee, David Booth, Pervaiz Alam,