An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
A tutorial on support vector regression
Statistics and Computing
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support vector machine techniques for nonlinear equalization
IEEE Transactions on Signal Processing
Empirical risk minimization for support vector classifiers
IEEE Transactions on Neural Networks
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To analyze the harmonics in power system efficiently, a new harmonic source model is proposed in this paper. And this new model takes advantage of support vector machine (SVM) theory to find the relationship between the harmonic current and all voltage components. Then a comparison between the linear regressive model and nonlinear regressive models with different kernel functions has been made. The computer simulation has revealed that the model implemented by the nonlinear regression with Polynomial kernel is more precise, and is superior to other regressions.