vector machine techniques for modeling of seismic liquefaction data

Clicks: 274
ID: 253938
2014
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Abstract
This article employs three soft computing techniques, Support Vector Machine (SVM); Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for prediction of liquefaction susceptibility of soil. SVM and LSSVM are based on the structural risk minimization (SRM) principle which seeks to minimize an upper bound of the generalization error consisting of the sum of the training error and a confidence interval. RVM is a sparse Bayesian kernel machine. SVM, LSSVM and RVM have been used as classification tools. The developed SVM, LSSVM and RVM give equations for prediction of liquefaction susceptibility of soil. A comparative study has been carried out between the developed SVM, LSSVM and RVM models. The results from this article indicate that the developed SVM gives the best performance for prediction of liquefaction susceptibility of soil.
Reference Key
samui2014ainvector Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Pijush Samui
Journal der pharmacia lettre
Year 2014
DOI
10.1016/j.asej.2013.12.004
URL
http://www.sciencedirect.com/science/article/pii/S2090447913001275
Keywords
support vector machine relevance vector machine civil engineering (general)

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