A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification-Reference-Cited by-同舟云学术

A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification

Published:2021-08 Issue:1 Volume:293 Page:24-35
ISSN:0377-2217
Container-title:European Journal of Operational Research
language:en
Short-container-title:European Journal of Operational Research

Author:

Jiménez-Cordero Asunción,Morales Juan Miguel,Pineda Salvador

Funder

European Research Council

Ministerio de Economía, Industria y Competitividad, Gobierno de España

Publisher

Elsevier BV

Subject

Information Systems and Management,Management Science and Operations Research,Modelling and Simulation,General Computer Science,Industrial and Manufacturing Engineering

Reference38 articles.

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5. Functional-bandwidth kernel for support vector machine with functional data: An alternating optimization algorithm;Blanquero;European Journal of Operational Research,2019

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