Abstract-Support vector machines (SVMs) have improved generalization performance over other classical optimization techniques. Here, we introduce an SVM-based approach for linear array processing and beamforming. The development of a modified cost function is presented and it is shown how it can be applied to the problem of linear beamforming. Finally, comparison examples are included to show the validity of the new minimization approach.

The estimation of the spectrum usage from the point of view of number of users and modulation types is addressed in this paper. The techniques used here are based on Support Vector Machines (SVM). SVMs are machine learning strategies which use a robust cost function alternative to the widely used Least Squares function and that apply a regularization which provides control of the complexity of the resulting estimators. As a result, estimators are robust against interferences and nongaussian noise and present excellent generalization properties where the number of data available for the estimation is small. The structure pre sented here has a feature extraction part that, instead of using an FFT approach, uses the SVM criterion for spectrum estimation, feature extraction and modulation classification .

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In this paper, a nonlinear system identification based on support vector machines (SVM) has been addressed. A family of SVM-ARMA models is presented in order to integrate the input and the output in the reproducing kernel Hilbert space (RKHS). The performances of the different SVM-ARMA formulations for system identification are illustrated with two systems and compared with the Least Square method.

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