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Abstract: Support vector machines (SVMs) have played a key role in broad classes of problems arising in various fields. Much more recently, SVMs have become the tool of choice for problems arising in data classification and mining. This paper emphasizes some recent developments that the author and his colleagues have contributed to such as: generalized SVMs (a very general mathematical programming framework for SVMs), smooth SVMs (a smooth nonlinear equation representation of SVMs solvable by a fast... (Update)
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BibTeX entry: (Update)
@misc{ mangasarian-data,
author = "O. L. Mangasarian",
title = "Data Mining via Support Vector Machines",
url = "citeseer.ist.psu.edu/513524.html" }
Citations (may not include all citations):124 Robust linear programming discrimination of two linearly ins.. - Bennett, Mangasarian - 1992
72 Minimization of functions having Lipschitz-continuous first .. (context) - Armijo - 1966
50 Feature selection via concave minimization and support vecto.. - Bradley, Mangasarian - 1998
Documents on the same site (http://www.cs.wisc.edu/~olvi/): More
Smoothing Methods for Convex Inequalities and Linear.. - Chen, Mangasarian (1995) (Correct)
Arbitrary-Norm Separating Plane - Mangasarian (1997) (Correct)
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