Lane Change Intention Inference of Surrounding Vehicle: Comparative Study on Relevance Vector Machine (RVM) and Support Vector Machine (SVM)

2021 ◽  
Author(s):  
Jin Ho Yang ◽  
Dae Jung Kim ◽  
Chung Choo Chung
Keyword(s):  
Support Vector Machine ◽  
Comparative Study ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Lane Change ◽  
Intention Inference

Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils

2016 ◽  
pp. 124-160 ◽  
Author(s):  
J. Jagan ◽  
Prabhakar Gundlapalli ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Earthquake Engineering ◽  
Relevance Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Ground Acceleration ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Input Variables

The determination of liquefaction susceptibility of soil is a paramount project in geotechnical earthquake engineering. This chapter adopts Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Least Square Support Vector Machine (LSSVM) for determination of liquefaction susceptibility based on Cone Penetration Test (CPT) from Chi-Chi earthquake. Input variables of SVM, RVM and LSSVM are Cone Resistance (qc) and Peak Ground Acceleration (amax/g). SVM, RVM and LSSVM have been used as classification tools. The developed SVM, RVM and LSSVM give equations for determination of liquefaction susceptibility of soil. The comparison between the developed models has been carried out. The results show that SVM, RVM and LSSVM are the robust models for determination of liquefaction susceptibility of soil.

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