A novel embedded min-max approach for feature selection in nonlinear
support vector machine classification

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A novel embedded min-max approach for feature selection in nonlinear support vector machine classification

21 April 2020

Asunción Jiménez-Cordero

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Papers citing "A novel embedded min-max approach for feature selection in nonlinear support vector machine classification"

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Title
Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence John Violos Konstantina-Christina Diamanti I. Kompatsiaris Symeon Papadopoulos 65 0 0 02 Jun 2025
A Robust Support Vector Machine Approach for Raman COVID-19 Data Classification Marco Piazza Andrea Spinelli Francesca Maggioni Marzia Bedoni Enza Messina 45 0 0 29 Jan 2025
Smooth Ranking SVM via Cutting-Plane Method E. C. Ozcan Berk Görgülü M. Baydogan I. Paschalidis 62 0 0 25 Jan 2024
Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds Yong Cai Michael I. Jordan Tianyi Lin Argyris Oikonomou Emmanouil-Vasileios Vlatakis-Gkaragkounis 75 4 0 29 Jun 2023
A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification R. Leone Francesca Maggioni Andrea Spinelli 45 4 0 09 Jun 2023
Margin Optimal Classification Trees Federico DÓnofrio G. Grani Marta Monaci L. Palagi 77 10 0 19 Oct 2022
Feature subset selection for kernel SVM classification via mixed-integer optimization Ryuta Tamura Yuichi Takano Ryuhei Miyashiro 49 2 0 28 May 2022
Multiclass Optimal Classification Trees with SVM-splits V. Blanco Alberto Japón J. Puerto 55 6 0 16 Nov 2021
Optimization with Constraint Learning: A Framework and Survey Adejuyigbe O. Fajemisin Donato Maragno D. Hertog 111 52 0 05 Oct 2021