scholarly journals Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares

2006 ◽  
pp. 11-20 ◽  
Author(s):  
Shutao Li ◽  
Chen Liao ◽  
James T. Kwok
Keyword(s):  
Feature Extraction ◽  
Least Squares ◽  
Partial Least Squares ◽  
T Test ◽  
Test Statistics ◽  
Kernel Partial Least Squares ◽  
Gene Feature

Research on Relevance Vector Machine Model Based on Kernel Partial Least Square in Properties of Engineering Materials

2014 ◽  
Vol 952 ◽  
pp. 311-314
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Hai Long Gao
Keyword(s):  
Feature Extraction ◽  
Least Squares ◽  
Partial Least Squares ◽  
Relevance Vector Machine ◽  
Partial Least Square ◽  
Least Square ◽  
Machine Model ◽  
Prediction Of Properties ◽  
Kernel Partial Least Squares ◽  
Engineering Materials

To improve the prediction of properties of engineering materials, a Relevance Vector Machine (RVM) regression algorithm based on Kernel Partial Least Squares (KPLS) is proposed. In the algorithm, firstly execute the feature extraction from the original samples using KPLS, and then use obtained feature to realize RVM regression. The simulation shows that the hybrid regression algorithm can effectively reduce the difficulty on RVM modeling and has a wide application in prediction of properties of engineering materials.

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