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[th_column]Título:[/th_column]
[th_column]Geometric Intuition and Algorithms for Ev-SVM. Journal of Machine Learning Research. 323−369. JMLR.[/th_column]
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[row_column]Autor:[/row_column]
[row_column]Barbero, A., Takeda, A., & López, J. [/row_column]
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[table_row]
[row_column]Año:[/row_column]
[row_column]2015[/row_column]
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[table_row]
[row_column]Enlace:[/row_column]
[row_column]http://dl.acm.org/citation.cfm?id=2789283[/row_column]
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Abstract
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In this work we address the Ev-SVM model proposed by Pérez-Cruz et al. as an extension of the traditional v support vector classification model (v-SVM). Through an enhancement of the range of admissible values for the regularization parameter v, the Ev-SVM has been shown to be able to produce a wider variety of decision functions, giving rise to a better adaptability to the data. However, while a clear and intuitive geometric interpretation can be given for the v-SVM model as a nearest-point problem in reduced convex hulls (RCH-NPP), no previous work has been made in developing such intuition for the Ev-SVM model. In this paper we show how Ev-SVM can be reformulated as a geometrical problem that generalizes RCH-NPP, providing new insights into this model. Under this novel point of view, we propose the RapMinos algorithm, able to solve Ev-SVM more efficiently than the current methods. Furthermore, we show how RapMinos is able to address the Ev-SVM model for any choice of regularization norm lp ≥1 seamlessly, which further extends the SVM model flexibility beyond the usual Ev-SVM models.
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