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Neural Networks, IEEE - INNS - ENNS International Joint Conference on (2000)
Como, Italy
July 24, 2000 to July 27, 2000
ISSN: 1098-7576
ISBN: 0-7695-0619-4
pp: 6354
Alan Fan , University of Melbourne
Marimuthu Palaniswami , University of Melbourne
ABSTRACT
Conventional Neural Network approach has been found useful in predicting corporate distress from financial statements. In this paper, we have adopted a Support Vector Machine approach to the problem. A new way of selecting bankruptcy predictors is shown, using the Euclidean distance based criterion calculated within the SVM kernel. A comparative study is pro vided using three classical corporate distress models and an alternative model based on the SVM approach.
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CITATION
Alan Fan, Marimuthu Palaniswami, "Selecting Bankruptcy Predictors Using a Support Vector Machine Approach", Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 06, no. , pp. 6354, 2000, doi:10.1109/IJCNN.2000.859421
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