A Support Vector Machine Signal Estimation Framework

Support vector machine (SVM) were originally conceived as efficient methods for pattern recognition and classification, and the SVR was subsequently proposed as the SVM implementation for regression and function approximation. Nowadays, the SVR and other kernel‐based regression methods have become a mature and recognized tool in digital signal processing (DSP). This chapter starts to pave the way to treat all the problems within the field of kernel machines, and presents the fundamentals for a simple, framework for tackling estimation problems in DSP using support vector machine SVM. It outlines the particular models and approximations defined within the framework. The chapter concludes with some examples of use of support vector regression (SVR) in given problems, and on primal signal model (PSM) problem statements. Particular signal space expansions are given for nonparametric spectral analysis, for system identification and time series prediction, for digital communications, for convolutional models, and for array processing (temporal reference).