ABSTRACT
The objective of this paper is to use support Vecor Machine(SVM) for three dimensional (3D) site characterization model for Suurpelto based on a large amount Cone Resistance(qc) values in an area of 325 hectares. Cone penetration test(CPT) has been done at five points. The Support Vector Machine (SVM) based on statistical learning theory has been developed by Vapnik (1995). It provides a new, efficient novel approach to improve the generalization performance and can attain a global minimum. In general, SVMs have been used for pattern recognition problems. Recently it has been used to solve non-linear regression estimation and time series prediction by introducing ε-insensitive loss function (Mukherjee et al. 1997; Muller et al. 1997; Vapnik 1995; Vapnik et al. 1997). The SVM implements the structural risk minimization principle (SRMP), which has been shown to be superior to the more traditional Empirical Risk Minimization Principle (ERMP) employed by many of the other modelling techniques (Osuna et al. 1997; Gunn 1998). SRMP minimizes an upper bound of the generalization error whereas, ERMP minimizes the training error. In this way, it produces the better generalization than traditional techniques.
