A sparse method for least squares twin support vector regression

Neurocomputing ◽

10.1016/j.neucom.2015.12.133 ◽

2016 ◽

Vol 211 ◽

pp. 150-158 ◽

Author(s):

Huajuan Huang ◽

Xiuxi Wei ◽

Yongquan Zhou

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Support Vector ◽

Twin Support Vector Regression

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Robust Least Squares Twin Support Vector Regression With Adaptive FOA and PSO for Short-Term Traffic Flow Prediction

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2021.3130264 ◽

2021 ◽

pp. 1-15

Author(s):

He Yan ◽

Yong Qi ◽

Qiaolin Ye ◽

Dong-Jun Yu

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Traffic Flow ◽

Support Vector ◽

Twin Support Vector Regression ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Robust Least Squares

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Primal least squares twin support vector regression

Journal of Zhejiang University SCIENCE C ◽

10.1631/jzus.ciip1301 ◽

2013 ◽

Vol 14 (9) ◽

pp. 722-732 ◽

Author(s):

Hua-juan Huang ◽

Shi-fei Ding ◽

Zhong-zhi Shi

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Support Vector ◽

Twin Support Vector Regression

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Prediction of uniaxial compressive strength of rock samples using density weighted least squares twin support vector regression

Neural Computing and Applications ◽

10.1007/s00521-021-06204-2 ◽

2021 ◽

Author(s):

Deepak Gupta ◽

N. Natarajan

Keyword(s):

Compressive Strength ◽

Least Squares ◽

Support Vector Regression ◽

Uniaxial Compressive Strength ◽

Weighted Least Squares ◽

Support Vector ◽

Twin Support Vector Regression ◽

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Improving the optimization efficiency and precision of least squares support vector regression (LSSVR) for pear property prediction

10.1117/12.865737 ◽

2010 ◽

Author(s):

Yong Hao ◽

Yande Liu ◽

Hailiang Zhang ◽

Xuemei Liu ◽

Yuanyuan Pan

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Support Vector ◽

Property Prediction ◽

Optimization Efficiency

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Rapid measurement method for lithium‐ion battery state of health estimation based on least squares support vector regression

International Journal of Energy Research ◽

10.1002/er.6194 ◽

2020 ◽

Author(s):

Bin Xiao ◽

Bing Xiao ◽

Luoshi Liu

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Lithium Ion Battery ◽

Measurement Method ◽

Lithium Ion ◽

Support Vector ◽

State Of Health ◽

Rapid Measurement

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Application of QSPR for prediction of percent conversion of esterification reactions in supercritical carbon dioxide using least squares support vector regression

The Journal of Supercritical Fluids ◽

10.1016/j.supflu.2010.04.007 ◽

2010 ◽

Vol 54 (2) ◽

pp. 222-230 ◽

Author(s):

M. Esteki ◽

M. Rezayat ◽

H.S. Ghaziaskar ◽

T. Khayamian

Keyword(s):

Carbon Dioxide ◽

Supercritical Carbon Dioxide ◽

Least Squares ◽

Support Vector Regression ◽

Support Vector ◽

Percent Conversion ◽

Esterification Reactions ◽

Supercritical Carbon

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Goodnews Bay Platinum Resource Estimation Using Least Squares Support Vector Regression with Selection of Input Space Dimension and Hyperparameters

Natural Resources Research ◽

10.1007/s11053-011-9140-6 ◽

2011 ◽

Vol 20 (2) ◽

pp. 117-129 ◽

Author(s):

Snehamoy Chatterjee ◽

Sukumar Bandopadhyay

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Space Dimension ◽

Support Vector ◽

Resource Estimation ◽

Input Space ◽

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A New Fast Twin Support Vector Regression

2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS) ◽

10.1109/icsess49938.2020.9237692 ◽

2020 ◽

Author(s):

Ying Fan ◽

YiLin Shi ◽

Kai Kang ◽

FengDe Zheng ◽

Peng Su ◽

...

Keyword(s):

Support Vector Regression ◽

Support Vector ◽

Twin Support Vector Regression

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Twin Support Vector Regression for Prediction of Natural Gas Hydrate Formation Conditions

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.1c03534 ◽

2021 ◽

Author(s):

Changzhou Li

Keyword(s):

Natural Gas ◽

Support Vector Regression ◽

Gas Hydrate ◽

Hydrate Formation ◽

Support Vector ◽

Natural Gas Hydrate ◽

Twin Support Vector Regression ◽

Gas Hydrate Formation ◽

Formation Conditions

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Regularized Least Squares Fuzzy Support Vector Regression for Time Series Forecasting

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.246736 ◽

2006 ◽

Author(s):

Jayadeva ◽

R. Khemchandani ◽

S. Chandra

Keyword(s):

Time Series ◽

Least Squares ◽

Support Vector Regression ◽

Time Series Forecasting ◽

Support Vector ◽

Regularized Least Squares

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