An Advanced Least Squares Twin Multi-class Classification Support Vector Machine for Few-Shot Classification

2019 ◽  
pp. 243-252
Author(s):  
Yu Li ◽  
Zhonggeng Liu ◽  
Huadong Pan ◽  
Jun Yin ◽  
Xingming Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector ◽  
Shot Classification ◽  
Multi Class Classification

Improvements on least squares twin multi-class classification support vector machine

2018 ◽  
Vol 313 ◽  
pp. 196-205 ◽  
Author(s):  
Márcio Dias de Lima ◽  
Nattane Luiza Costa ◽  
Rommel Barbosa
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector ◽  
Multi Class Classification

Least squares twin multi-class classification support vector machine

2015 ◽  
Vol 48 (3) ◽  
pp. 984-992 ◽  
Author(s):  
Jalal A. Nasiri ◽  
Nasrollah Moghadam Charkari ◽  
Saeed Jalili
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector ◽  
Multi Class Classification

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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