most citedFeature subset selection for kernel SVM classification via mixed-integer optimization

2 citations · 2 across the 2 of their papers we have counts for

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2 papers

math.OC2026

Cross-validation-based optimal feature selection for linear SVM classification

Masaharu Mori, Shunnosuke Ikeda, Ryuta Tamura +2

This paper addresses feature subset selection for Support Vector Machines (SVMs) based on the cross-validation criterion. Unlike statistical criteria such as the Akaike information…

cs.LG2022★ 2 cited

Feature subset selection for kernel SVM classification via mixed-integer optimization

Ryuta Tamura, Yuichi Takano, Ryuhei Miyashiro

We study the mixed-integer optimization (MIO) approach to feature subset selection in nonlinear kernel support vector machines (SVMs) for binary classification. First proposed for…