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BibTeX entry
@Article{Murrell:2011:FDK,
author = "Hugh Murrell and Kazuo Hashimoto and Daichi Takatori",
title = "{Fisher} Discrimination with Kernels",
journal = j-MATHEMATICA-J,
volume = "13",
number = "??",
pages = "??--??",
month = "????",
year = "2011",
CODEN = "????",
ISSN = "1047-5974 (print), 1097-1610 (electronic)",
bibdate = "Sat Mar 15 08:18:42 MDT 2014",
bibsource = "http://www.math.utah.edu/pub/tex/bib/mathematicaj.bib",
URL = "http://www.mathematica-journal.com/2011/07/fisher-discrimination-with-kernels/",
abstract = "Fisher first introduced the Fisher linear discriminant
back in 1938. After the popularization of the support
vector machine (SVM) and the kernel trick it became
inevitable that the Fisher linear discriminant would be
kernelized. Sebastian Mika accomplished this task as
part of his Ph.D. in 2002 and the kernelized Fisher
discriminant (KFD) now forms part of the large-scale
machine-learning tool Shogun. In this article we
introduce the package MathKFD. We apply MathKFD to
synthetic datasets to demonstrate nonlinear
classification via kernels. We also test performance on
datasets from the machine-learning literature. The
construction of MathKFD follows closely in style the
construction of MathSVM by Nilsson and colleagues. We
hope these two packages and others of the same ilk will
eventually be integrated to form a kernel-based
machine-learning environment for Mathematica.",
acknowledgement = ack-nhfb,
journal-URL = "http://www.mathematica-journal.com/",
}
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