The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Efficient SVM Regression Training with SMO
Machine Learning
A tutorial on support vector regression
Statistics and Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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This paper investigates modeling of the pulsed gas tungsten arc welding (GTAW) process using support vector machine (SVM). Modeling is one of the key techniques in the control of the arc welding process, but is still a very difficult problem because the process is multivariable, time-delay and nonlinear. We analyze the characteristics of SVM for solving the challenge problem and give the main steps of modeling, including selecting input/output variables, kernel function and parameters according to our specific problem. Experimental results of the SVM, neural network and rough set methods show the feasibility and superiority of our approach.