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Switching Neural Network: An application to Regression Problems
Enrico Ferrari, Marco Muselli
14 - 21
10.3233/978-1-60750-692-8-14
Frontiers in Artificial Intelligence and Applications
Volume 226: Neural Nets WIRN10
Abstract

In this paper regression problems, in which the output is continuous or discrete, are considered. In particular, the Switching Neural Network approach, which has been introduced for classification, is properly extended to deal with regression tasks. The resulting model, named SNN-reg, presents multiple advantages, involving both the quality of the obtained solution and the computational effort needed for its generation. Moreover, SNN-reg allows a regression function to be swritten in terms of a set of intelligible rules, which can be interpreted by the user.

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