SVM-based glioma grading: optimization by feature reduction analysis

We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation co...

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Bibliographic Details
Main Authors:	Zöllner, Frank G. (Author) , Schad, Lothar R. (Author)
Format:	Article (Journal)
Language:	English
Published:	13 April 2012
In:	Zeitschrift für medizinische Physik Year: 2012, Volume: 22, Issue: 3, Pages: 205-214
ISSN:	1876-4436
DOI:	10.1016/j.zemedi.2012.03.007
Online Access:	Verlag, Volltext: http://dx.doi.org/10.1016/j.zemedi.2012.03.007 Verlag, Volltext: http://www.sciencedirect.com/science/article/pii/S0939388912000360
Author Notes:	Frank G. Zöllner, Kyrre E. Emblem, Lothar R. Schad

SVM-based glioma grading: optimization by feature reduction analysis by Zöllner, Frank G. (Author) , Emblem, Kyrre E. (Author) , Schad, Lothar R. (Author) ,

in: Zeitschrift für medizinische Physik, 22 (2012), 3, Seite 205-214

Article (Journal)

SVM-based glioma grading: optimization by feature reduction analysis

SVM-based glioma grading: optimization by feature reduction analysis by Zöllner, Frank G. (Author) , Emblem, Kyrre E. (Author) , Schad, Lothar R. (Author) ,

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