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Simple and robust methods for support vector expansions

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3 Author(s)
Mattera, D. ; Dipt. di Ingegneria Elettronica e delle Telecomunicazioni, Naples Univ., Italy ; Palmieri, F. ; Haykin, Simon

Most support vector (SV) methods proposed in the recent literature can be viewed in a unified framework with great flexibility in terms of the choice of the kernel functions and their constraints. We show that all these problems can be solved within a unique approach if we are equipped with a robust method for finding a sparse solution of a linear system. Moreover, for such a purpose, we propose an iterative algorithm that can be simply implemented. Finally, we compare the classical SV approach with other, recently proposed, cross-correlation based, alternative methods. The simplicity of their implementation and the possibility of exactly calculating their computational complexity constitute important advantages in a real-time signal processing scenario

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Neural Networks, IEEE Transactions on  (Volume:10 ,  Issue: 5 )