Abstract
In this paper a novel classification method for real world classification tasks is proposed. The method was designed to overcome the difficulties encountered by traditional methods when coping with those real world problems where the key issue is the detection of particular situations - such as for instance machine faults or anomalies - which in some frameworks are hard to be recognized due to some interacting factors that are analyzed within the paper. The method is described and tested on two industrial problems, which show the goodness of the proposed approach and encourage its use in the industrial environments.
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Vannucci, M., Colla, V., Sgarbi, M., Toscanelli, O. (2009). Thresholded Neural Networks for Sensitive Industrial Classification Tasks. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_165
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DOI: https://doi.org/10.1007/978-3-642-02478-8_165
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02477-1
Online ISBN: 978-3-642-02478-8
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