An Equivalence Between Sparse Approximation and Support Vector Machines

@article{Girosi1998AnEB,
  title={An Equivalence Between Sparse Approximation and Support Vector Machines},
  author={Federico Girosi},
  journal={Neural Computation},
  year={1998},
  volume={10},
  pages={1455-1480}
}
This article shows a relationship between two different approximation techniques: the support vector machines (SVM), proposed by V. Vapnik (1995) and a sparse approximation scheme that resembles the basis pursuit denoising algorithm (Chen, 1995; Chen, Donoho, & Saunders, 1995). SVM is a technique that can be derived from the structural risk minimization principle (Vapnik, 1982) and can be used to estimate the parameters of several different approximation schemes, including radial basis… CONTINUE READING
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