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Interpretable support vector machines for functional data

Support Vector Machines (SVMs) is known to be a powerful nonparametric classification technique even for high-dimensional data. Although predictive ability is important, obtaining an easy-to-interpret classifier is also crucial in many applications. Linear SVM provides a classifier based on a linear score. In the case of functional data, the coefficient function that defines such linear score usually has many irregular oscillations, making it difficult to interpret.
Year of publication:
2014
Authors: Martin-Barragan, Belen ; Lillo, Rosa ; Romo, Juan
Published in:
European Journal of Operational Research. - Elsevier, ISSN 0377-2217. - Vol. 232.2014, 1, p. 146-155
Publisher: Elsevier
Subject: Data mining | Interpretability | Classification | Linear programming | Regularization methods | Functional data analysis
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Type of publication: Article
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Persistent link: https://www.econbiz.de/10010738147
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