FN Archimer Export Format
PT J
TI Generalization of Parameter Selection of SVM and LS-SVM for Regression
BT 
AF Zeng, J
   Tan, ZH
   Matsunaga, T
   Shirai, T
AS 1:1;2:2;3:1;4:1;
FF 1:;2:;3:;4:;
C1 National Institute for Environmental Studies, Tsukuba, Ibaraki 305-0053, Japan
   Department of Environmental Science, Hainan University, Haikou 570228, China
C2 NIES, JAPAN
   UNIV HAINAN, CHINA
IN DOAJ
TC 8
UR https://archimer.ifremer.fr/doc/00676/78774/80949.pdf
LA English
DT Article
CR OISO - OCÉAN INDIEN SERVICE D'OBSERVATION
DE ;support vector machine for regression;SVM;LS-SVM;machine learning;parameter optimization;global ocean CO2
AB A Support Vector Machine (SVM) for regression is a popular machine learning model that aims to solve nonlinear function approximation problems wherein explicit model equations are difficult to formulate. The performance of an SVM depends largely on the selection of its parameters. Choosing between an SVM that solves an optimization problem with inequality constrains and one that solves the least square of errors (LS-SVM) adds to the complexity. Various methods have been proposed for tuning parameters, but no article puts the SVM and LS-SVM side by side to discuss the issue using a large dataset from the real world, which could be problematic for existing parameter tuning methods. We investigated both the SVM and LS-SVM with an artificial dataset and a dataset of more than 200,000 points used for the reconstruction of the global surface ocean CO2 concentration. The results reveal that: (1) the two models are most sensitive to the parameter of the kernel function, which lies in a narrow range for scaled input data; (2) the optimal values of other parameters do not change much for different datasets; and (3) the LS-SVM performs better than the SVM in general. The LS-SVM is recommended, as it has less parameters to be tuned and yields a smaller bias. Nevertheless, the SVM has advantages of consuming less computer resources and taking less time to train. The results suggest initial parameter guesses for using the models. 
PY 2019
PD JUL
SO Machine Learning And Knowledge Extraction
SN 2504-4990
PU MDPI AG
VL 1
IS 2
UT 000646930500001
BP 745
EP 755
DI 10.3390/make1020043
ID 78774
ER

EF