Polynomial and RBF kernels as marker selection tools-a breast cancer case study

URI	http://purl.tuc.gr/dl/dias/02F9A884-340C-403D-82CD-69BBA151D2EF	-
Identifier	https://doi.org/10.1109/ICMLA.2007.67	-
Language	en	-
Extent	6 pages	en
Title	Polynomial and RBF kernels as marker selection tools-a breast cancer case study	en
Creator	Zervakis Michalis	en
Creator	Ζερβακης Μιχαλης	el
Creator	M.E. Blazadonakis	en
Publisher	Institute of Electrical and Electronics Engineers	en
Content Summary	The problem of marker selection in DNA microarray experiment, due to the "curse of dimensionality", has been mostly addressed so far by linear approaches. Taking into account the fact that the domain of interest is a complex one, where non-linear interconnections and dependencies may also exist among the extremely large number of examined genes, we address the use of nonlinear tools to assess the problem. In this study, we propose to apply the kernel ability of Support Vector Machines in combination with Fisher's ratio as an alternative approach to assess the problem.	en
Type of Item	Αφίσα σε Συνέδριο	el
Type of Item	Conference Poster	en
License	http://creativecommons.org/licenses/by/4.0/	en
Date of Item	2015-10-24	-
Date of Publication	2007	-
Bibliographic Citation	M.E. Blazadonakis, M. Zervakis ,"Polynomial and RBF kernels as marker selection tools-a breast cancer case study ,"in 2007 Sixth Intern. Conf. on Machine Learn. and Applications, ICMLA.pp. 488-493.doi:10.1109/ICMLA.2007.67	en

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