Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine

2013 ◽  
Vol 36 (15) ◽  
pp. 2464-2471 ◽  
Author(s):  
Jun Yan ◽  
Jian-Hua Huang ◽  
Min He ◽  
Hong-Bing Lu ◽  
Rui Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Essential Oils ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Partial Least Squares ◽  
Retention Indices ◽  
Support Vector ◽  
Plant Essential Oils ◽  
Prediction Of Retention

Image Texture Classification Based on LS-SVM

2012 ◽  
Vol 182-183 ◽  
pp. 869-872
Author(s):  
Yan Ling Zhao ◽  
Xiao Shi Zheng ◽  
Guang Qi Liu ◽  
Na Li
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Least Squares ◽  
Texture Classification ◽  
Energy Ratio ◽  
Image Texture ◽  
Support Vector ◽  
Good Ability ◽  
Non Linear

LS-SVM (Least Squares Support Vector Machine) is simple and has a good ability of non-linear regression. As inputs of LS-SVM, DC-Energy-Ratio and Deviation of image samples are extracted first. Output of LS-SVM is the current texture classification. The results show that LS-SVM classifies images accurately by training the proposed two features.

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