Journal
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
Volume -, Issue -, Pages -Publisher
SPRINGER
DOI: 10.1155/2008/816507
Keywords
-
Categories
Ask authors/readers for more resources
Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution problems. Here, a sparse deconvolution algorithm based on the SVM framework for signal processing is presented and analyzed, including comparative evaluations of its performance from the points of view of estimation and detection capabilities, and of robustness with respect to non-Gaussian additive noise. Copyright (C) 2008 Jose Luis Rojo-Alvarez et al.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available