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Scoring Systems and Large Margin Perceptron Ranking

Scoring Systems and Large Margin Perceptron Ranking
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Author(s): Bernd-Juergen Falkowski (University of Applied Sciences Stralsund, Germany), Martin Appelt (University of Applied Sciences Stralsund, Germany), Christian Finger (University of Applied Sciences Stralsund, Germany), Sebastian Koch (University of Applied Sciences Stralsund, Germany) and Hendrik van der Linde (University of Applied Sciences Stralsund, Germany)
Copyright: 2007
Pages: 4
Source title: Managing Worldwide Operations and Communications with Information Technology
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59904-929-8.ch206
ISBN13: 9781599049298
EISBN13: 9781466665378

Abstract

Perceptron learning is proposed in the context of so-called scoring systems used for assessing creditworthiness as stipulated in the Basel II central banks capital accord of the G10-states. The approximate solution of a related ranking problem using a large margin algorithm is described. Some experimental results obtained by utilizing a Java prototype are exhibited. From these it becomes apparent that combining the large margin algorithm presented here with the pocket algorithm provides an attractive alternative to the use of support vector machines. Related algorithms are briefly discussed.

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