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RI | Publications | Classification Using Multi-Layered Perceptrons
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Classification Using Multi-Layered Perceptrons
S. Piramuthu, M.J. Shaw, and J.A. Gentry
tech. report CMU-RI-TR-90-29, Robotics Institute, Carnegie Mellon University, December, 1990.
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| Abstract |
There has been an increasing interest in the applicability of neural networks in disparate domains. In this paper, we describe the use of multi-layered perceptrons, a type of neural network topology, for financial classification problems, within promising results. Back-propagation, which is the learning algorithm most often used in multi-layered perceptrons, however, is inherently an inefficient search procedure. We present improved procedures which have much better convergence properties. Using several financial classification applications as examples, we show the efficacy of using multi-layered perceptrons with improved learning algorithms. The modified learning algorithms have better performance, in terms of classification/prediction accuracies, than the methods previously used in the literature, such as probit analysis and similarity-based learning techniques.
| Notes |
Grant ID: DACA 76-89-C-0014
Number of pages: 36
| Text Reference |
S. Piramuthu, M.J. Shaw, and J.A. Gentry, Classification Using Multi-Layered Perceptrons, tech. report CMU-RI-TR-90-29, Robotics Institute, Carnegie Mellon University, December, 1990.
| BibTeX Reference |
@techreport{Piramuthu_1990_241,
author = "Selwyn Piramuthu and Michael J Shaw and James A. Gentry",
title = "Classification Using Multi-Layered Perceptrons",
institution = "Robotics Institute, Carnegie Mellon University",
month = "December",
year = "1990",
number = "CMU-RI-TR-90-29",
address = "Pittsburgh, PA"
}