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An Equivalence Between Sparse Approximation and Support Vector Machines

1998
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Neural Computation
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This paper shows a relationship between two di erent approximation techniques: the Support Vector Machines (SVM), proposed by V . V apnik (1995), and a sparse approximation scheme that resembles the Basis Pursuit De-Noising algorithm (Chen, 1995 Chen, Donoho and Saunders, 1995). SVM is a technique which can be derived from the Structural Risk Minimization Principle (Vapnik, 1982) and can be used to estimate the parameters of several di erent approximation schemes, including Radial Basis

doi:10.1162/089976698300017269
pmid:9698353
fatcat:gcsqwivvx5hirncjq7cki6swci