A systematic literature review on support vector machines applied to regression

Daniel Mavilo Calderon Nieto, Erik Alex Papa Quiroz, Miguel Angel Cano Lengua

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This article aims to identify the current state of the art of the latest research related to models and algorithms in support vector machines for regression. For that, we use the methodology proposed by Kitchenham and Charter, in order to answer the following research questions: Q1: In which research areas is the support vector machine for regression most used? Q2. What optimization models are used to support vector machine for regression? Q3. What algorithms or optimization methods are used to solve support vector machine for regression? Q4. What nonconvex optimization models use support vector machine for regression? Q5. What optimization algorithms are used for nonconvex models to support vector machine for regression? We obtain valuable information about the questions to construct new models and algorithms in this research area.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665429146
DOI
EstadoPublicada - 2021
Publicado de forma externa
Evento5th IEEE Sciences and Humanities International Research Conference, SHIRCON 2021 - Lima, Perú
Duración: 17 nov. 202119 nov. 2021

Serie de la publicación

NombreProceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021

Conferencia

Conferencia5th IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
País/TerritorioPerú
CiudadLima
Período17/11/2119/11/21

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Publisher Copyright:
© 2021 IEEE.

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