SUPPORT VECTOR MACHINES IN STRUCTURAL ENGINEERING: A REVIEW

Engineering multilevel support vector machines

The computational complexity of solving nonlinear support vector machine (SVM) is prohibitive on large-scale data. In particular, this issue becomes very sensitive when the data represents additional difficulties such as highly imbalanced class sizes. Typically, nonlinear kernels produce significantly higher classification quality to linear kernels but introduce extra kernel and model parameter...

Support Vector Machines: Review and Applications in Civil Engineering

Support Vector Machines (SVM) are emerging techniques which have proven successful in many traditionally neural network (NN) dominated applications. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing the me...

STAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES

Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P

Structural Extensions of Support Vector Machines

Fast Support Vector Machines for Structural Kernels

In this paper, we propose three important enhancements of the approximate cutting plane algorithm (CPA) to train Support Vector Machines with structural kernels: (i) we exploit a compact yet exact representation of cutting plane models using directed acyclic graphs to speed up both training and classification, (ii) we provide a parallel implementation, which makes the training scale almost line...