Geometric correction based color image watermarking using fuzzy least squares support vector machine and Bessel K form distribution

2017 ◽  
Vol 134 ◽  
pp. 197-208 ◽  
Author(s):  
Chunpeng Wang ◽  
Xingyuan Wang ◽  
Chuan Zhang ◽  
Zhiqiu Xia
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Color Image ◽  
Image Watermarking ◽  
Support Vector ◽  
Geometric Correction ◽  
Color Image Watermarking ◽  
Bessel K Form

A geometric correction based robust color image watermarking scheme using quaternion Exponent moments

Optik ◽  
2014 ◽  
Vol 125 (16) ◽  
pp. 4456-4469 ◽  
Author(s):  
Hong-ying Yang ◽  
Yan Zhang ◽  
Pei Wang ◽  
Xiang-yang Wang ◽  
Chun-peng Wang
Keyword(s):  
Color Image ◽  
Image Watermarking ◽  
Geometric Correction ◽  
Color Image Watermarking ◽  
Watermarking Scheme

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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