A mixed effects least squares support vector machine model for classification of longitudinal data

2012 ◽  
Vol 56 (3) ◽  
pp. 611-628 ◽  
Author(s):  
Jan Luts ◽  
Geert Molenberghs ◽  
Geert Verbeke ◽  
Sabine Van Huffel ◽  
Johan A.K. Suykens
2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Chai ◽  
Jiangze Du ◽  
Kin Keung Lai ◽  
Yan Pui Lee

This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search), PSO (particle swarm optimization), and GA (genetic algorithm). Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.


Author(s):  
ANTONIO JOSÉ TENZA-ABRIL ◽  
ROSANA SATORRE-CUERDA ◽  
PATRICIA COMPAÑ-ROSIQUE ◽  
FRANCISCO JOSÉ NAVARRO-GONZÁLEZ ◽  
YOLANDA VILLACAMPA

2017 ◽  
Vol 97 (5) ◽  
pp. 698-708 ◽  
Author(s):  
Summer G Goodson ◽  
Sarah White ◽  
Alicia M Stevans ◽  
Sanjana Bhat ◽  
Chia-Yu Kao ◽  
...  

2017 ◽  
Vol 123 ◽  
pp. 217-228 ◽  
Author(s):  
Guangzao Huang ◽  
Zijiang Yang ◽  
Xiaojing Chen ◽  
Guoli Ji

2018 ◽  
Vol 33 (8) ◽  
pp. 1330-1335 ◽  
Author(s):  
Y. M. Guo ◽  
L. B. Guo ◽  
Z. Q. Hao ◽  
Y. Tang ◽  
S. X. Ma ◽  
...  

A hybrid sparse partial least squares and least-squares support vector machine model was proposed to improve the accuracy of iron ore analysis using LIBS.


2020 ◽  
Vol 35 (7) ◽  
pp. 1487-1487
Author(s):  
Y. M. Guo ◽  
L. B. Guo ◽  
Z. Q. Hao ◽  
Y. Tang ◽  
S. X. Ma ◽  
...  

Correction for ‘Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model’ by Y. M. Guo et al., J. Anal. At. Spectrom., 2018, 33, 1330–1335, DOI: 10.1039/C8JA00119G.


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