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Using heavy-tailed distributions to stress-test kernel methods for segregating the firms that are likely to survive

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2 Author(s)
Hosseinizadeh, P. ; Mech. & Ind. Eng. Dept., Ryerson Univ., Toronto, ON, Canada ; Guergachi, A.

While kernel-based learning methods have emerged during the last two decades as major tools to effectively manage uncertainty, heavy-tailed distributions remain a major challenge for modelers who aim to predict the future behavior of complex systems. In this article, Weibull distribution has been used to stress-test kernel-based methods and study more specifically the impact of heavy-tailed distributions on the performance of Fisher kernels in identifying the potential for collapse of an enterprise based on its stock price.

Published in:

Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on

Date of Conference:

11-14 Oct. 2009

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