Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction

2012 ◽  
Vol 16 (8) ◽  
pp. 1347-1351 ◽  
Author(s):  
Pijush Samui ◽  
Pradeep Kurup
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Multivariate Adaptive Regression Spline ◽  
Support Vector ◽  
Regression Spline ◽  
Adaptive Regression

Multivariate Adaptive Regression Spline and Least Square Support Vector Machine for Prediction of Undrained Shear Strength of Clay

2012 ◽  
Vol 3 (2) ◽  
pp. 33-42 ◽  
Author(s):  
Pijush Samui ◽  
Pradeep Kurup
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Undrained Shear Strength ◽  
Least Square ◽  
Support Vector ◽  
Regression Spline ◽  
Adaptive Regression ◽  
Undrained Shear

This study adopts Multivariate Adaptive Regression Spline (MARS) and Least Square Support Vector Machine (LSSVM) for prediction of undrained shear strength (su) of clay, based Cone Penetration Test (CPT) data. Corrected cone resistance (qt), vertical total stress (sv), hydrostatic pore pressure (u0), pore water pressure at the cone tip (u1), and pore water pressure just above the cone base (u2) are used as input parameters for building the MARS and LSSVM models. The developed MARS and LSSVM models give simple equations for prediction of su. A comparative study between MARS and LSSSM is presented. The results confirm that the developed MARS and LSSVM models are robust for prediction of su.

Q-learning System Based on Cooperative Least Squares Support Vector Machine

2009 ◽  
Vol 35 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Xue-Song WANG ◽  
Xi-Lan TIAN ◽  
Yu-Hu CHENG ◽  
Jian-Qiang YI
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Learning System ◽  
Support Vector ◽  
Q Learning

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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