DocumentCode :
527493
Title :
A novel DoubleMinOver classifier based On second-order tensor
Author :
Wang, Zhe ; Han, Rui ; Pan, Zhisong ; Ni, Xuelei
Author_Institution :
Dept. of Comput. Sci. & Eng., East China Univ. of Sci. & Technol., Shanghai, China
Volume :
2
fYear :
2010
fDate :
10-12 Aug. 2010
Firstpage :
1053
Lastpage :
1057
Abstract :
It is well-known that the DoubleMinOver classifier as an extendibility of MinOver was developed to provide a maximum margin solution with a bias. But, it can be found that the DoubleMinOver algorithm only classifies the vector pattern and fails to work in the pattern represented with the second-order tensor. In this paper, we propose a novel DoubleMinOver classifier named STDoubleMinOver that can directly classify the second-order tensor pattern. Compared with the existing DoubleMinOver with only one weight w, the proposed STDoubleMinOver induces two weight vectors u and v so as to deal with the second-order tensor. The experiments here have demonstrated that the proposed STDoubleMinOver has a superior classification to the vectorized DoubleMinOver.
Keywords :
feature extraction; neural nets; pattern classification; STDoubleMinOver; doubleminover classifier; second-order tensor; Accuracy; Artificial neural networks; Feature extraction; Iris; Lenses; Tensile stress; Training; Classifier Design; DoubleMinOver; Second-order Tensor; Vector Pattern;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
Type :
conf
DOI :
10.1109/ICNC.2010.5582980
Filename :
5582980
Link To Document :
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