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The purpose of the article is to present Support Vector Machines (SVM) as a potentially useful tool in evaluation of bankruptcy risk and bankruptcy prediction. Invented by Vapnik, SVM method can be seen as a generalization of the classification by discriminant hyperplanes. In recent years, this method has gained high popularity in a number of applications where the problem of data classification is considered, including the task of bankruptcy prediction. Due to its good theoretical properties and high performance, this method has been applied in a number of problems where data classification is considered, including the task of bankruptcy prediction. In particular Platt's method can be used to obtain estimation of probability of bankruptcy. In the article we will present empirical results leading to the analysis of financial indicators of some companies.
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