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231223s2007 xx ||||| 00| ||eng c |
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Zafeiriou, Stefanos
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Minimum class variance support vector machines
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| 264 |
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|c 2007
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 13.11.2007
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|a Date Revised 10.12.2019
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a In this paper, a modified class of support vector machines (SVMs) inspired from the optimization of Fisher's discriminant ratio is presented, the so-called minimum class variance SVMs (MCVSVMs). The MCVSVMs optimization problem is solved in cases in which the training set contains less samples that the dimensionality of the training vectors using dimensionality reduction through principal component analysis (PCA). Afterward, the MCVSVMs are extended in order to find nonlinear decision surfaces by solving the optimization problem in arbitrary Hilbert spaces defined by Mercer's kernels. In that case, it is shown that, under kernel PCA, the nonlinear optimization problem is transformed into an equivalent linear MCVSVMs problem. The effectiveness of the proposed approach is demonstrated by comparing it with the standard SVMs and other classifiers, like kernel Fisher discriminant analysis in facial image characterization problems like gender determination, eyeglass, and neutral facial expression detection
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|a Evaluation Study
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Tefas, Anastasios
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pitas, Ioannis
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1997
|g 16(2007), 10 vom: 07. Okt., Seite 2551-64
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|x 1941-0042
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