TY - GEN
T1 - A systematic literature review on support vector machines applied to regression
AU - Nieto, Daniel Mavilo Calderon
AU - Quiroz, Erik Alex Papa
AU - Cano Lengua, Miguel Angel
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Support vector regression (SVR)
KW - non-convex optimization models
KW - optimization algorithms
UR - https://www.scopus.com/pages/publications/85131708728
U2 - 10.1109/SHIRCON53068.2021.9652268
DO - 10.1109/SHIRCON53068.2021.9652268
M3 - Conference contribution
AN - SCOPUS:85131708728
T3 - Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
BT - Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
T2 - 5th IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
Y2 - 17 November 2021 through 19 November 2021
ER -