A systematic literature review on support vector machines applied to regression

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665429146
DOIs
StatePublished - 2021
Externally publishedYes
Event5th IEEE Sciences and Humanities International Research Conference, SHIRCON 2021 - Lima, Peru
Duration: 17 Nov 202119 Nov 2021

Publication series

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

Conference

Conference5th IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
Country/TerritoryPeru
CityLima
Period17/11/2119/11/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • non-convex optimization models
  • optimization algorithms
  • Support vector regression (SVR)

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