Abstract
Fisher’s linear discriminant function (LDF) and related classifiers for binary and multiclass learning problems have performed well for many years and for many data sets. Recently, a brand-new learning methodology, support vector machines (SVMs), has emerged (Boser, Guyon, and Vapnik, 1992), which has matched the performance of the LDF and, in many instances, has proved to be superior to it.
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© 2013 Springer Science+Business Media New York
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Izenman, A.J. (2013). Support Vector Machines. In: Modern Multivariate Statistical Techniques. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-78189-1_11
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DOI: https://doi.org/10.1007/978-0-387-78189-1_11
Publisher Name: Springer, New York, NY
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Online ISBN: 978-0-387-78189-1
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