Edgar Osuna, Robert Freund, Federico Girosi  Home/Search
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Abstract: We investigate the problem of training a Support Vector Machine (SVM) [1, 2, 7] on a very large date base (e.g. 50,000 data points) in the case in which the number of support vectors is also very large (e.g. 40,000). Training a SVM is equivalent to solving a linearly constrained quadratic programming (QP) problem in a number of variables equal to the number of data points. This optimization problem is known to be challenging when the number of data points exceeds few thousands. In previous... (Update)
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BibTeX entry: (Update)
E. Osuna, R. Freund, and F. Girosi. Improved training algorithm for support vector machines. NNSP'97, 1997. http://citeseer.ist.psu.edu/osuna97improved.html More
@misc{ osuna97improved,
author = "E. Osuna and R. Freund and F. Girosi",
title = "Improved training algorithm for support vector machines",
text = "E. Osuna, R. Freund, and F. Girosi. Improved training algorithm for support
vector machines. NNSP'97, 1997.",
year = "1997",
url = "citeseer.ist.psu.edu/osuna97improved.html" }
Citations (may not include all citations):1291 The Nature of Statistical Learning Theory (context) - Vapnik - 1995
524 Support vector networks - Cortes, Vapnik - 1995
255 A training algorithm for optimal margin classifier - Boser, Guyon et al. - 1992
80 Support vector machines: Training and applications - Osuna, Freund et al. - 1997
72 Extracting support data for a given task - Scholkopf, Burges et al. - 1995
37 Large-scale linearly constrained optimization (context) - Murtagh, Saunders - 1978
7 Identifying speakers with support vectors networks - Schmidt - 1996
4 Non-linear predictive models for intra-day foreign exchange .. (context) - Zhang - 1993
The graph only includes citing articles where the year of publication is known.
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