J4 ›› 2010, Vol. 45 ›› Issue (11): 5-11.
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YANG Bing, WANG Shi-tong*
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Abstract:
Algorithm total margin v minimum class variance support vector machines based on common vectors (TM-v-M(CV)2SVMs) were presented for noisy face recognition, which integrates the advantages of minimum class variance support vector machines(MCVSVMs)and total margin v support vector machine(TM-v-SVM). Based on common vectors (CVs), the divergence matrix was introduced to improve the classification and anti-noisy performances of noisy face classification, and TM-v-M (CV)2SVMs derivation was given. The experimental results about noisy face classification showed that the proposed TM-v-M(CV)2SVMs had better classification performance than both the MCVSVMs and TM-v-SVM.
Key words: support vector machine; minimum class variance support vector machines; total margin v support vector machine; discriminative common vectors; common vectors; face recognition
YANG Bing, WANG Shi-tong*. Total margin v minimum class variance support vector machines based on common vectors for noisy face classification[J].J4, 2010, 45(11): 5-11.
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