scholarly journals Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

2018 ◽  
Vol 10 (4) ◽  
pp. 958 ◽  
Author(s):  
Shuyu Dai ◽  
Dongxiao Niu ◽  
Yaru Han
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Co2 Emissions ◽  
Support Vector ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping

Early Cancer Detection from Genome-wide Cell-free DNA Fragmentation via Shuffled Frog Leaping Algorithm and Support Vector Machine

2021 ◽  
Author(s):  
Linjing Liu ◽  
Xingjian Chen ◽  
Ka-Chun Wong
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Early Cancer ◽  
Machine Learning Algorithms ◽  
Early Cancer Detection ◽  
Supplementary Information ◽  
Support Vector ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping

Abstract Motivation Early cancer detection is significant for the patient mortality rate reduction. Although machine learning has been widely employed in that context, there are still deficiencies. In this work, we studied different machine learning algorithms for early cancer detection and proposed an Adaptive Support Vector Machine (ASVM) method by synergizing Shuffled Frog Leaping Algorithm (SFLA) and Support Vector Machine (SVM) in this paper. Results As ASVM regulates SVM for parameter adaption based on data characteristics, the experimental results demonstrated the robust generalization capability of ASVM on different datasets under different settings; for instance, ASVM can enhance the sensitivity by over 10% for early cancer detection compared with SVM. Besides, our proposed ASVM outperformed Grid Search + SVM and Random Search + SVM by significant margins in terms of the area under the ROC curve (AUC) (0.938 vs. 0.922 vs. 0.921). Availability The proposed algorithm and dataset are available at https://github.com/ElaineLIU-920/ASVM-for-Early-Cancer-Detection. Supplementary information Supplementary data are available at Bioinformatics online.

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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