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Expert Systems with Applications: An International Journal
Face Recognition Based on Wavelet-Curvelet-Fractal Technique
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Two-dimensional maximum margin feature extraction for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Reducing the run-time complexity of support vector data descriptions
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO
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Applied Soft Computing
Fast support vector data descriptions for novelty detection
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Local class boundaries for support vector machine
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IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Density-induced margin support vector machines
Pattern Recognition
Expert Systems with Applications: An International Journal
Fuzzy cost support vector regression on the fuzzy samples
Applied Intelligence
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Expert Systems with Applications: An International Journal
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Applied Soft Computing
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This paper presents a new classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that deals with several problems that may occur in support vector machines (SVMs) when applied to the face recognition. The proposed TAF-SVM not only solves the overfitting problem resulted from the outlier with the approach of fuzzification of the penalty, but also corrects the skew of the optimal separating hyperplane due to the very imbalanced data sets by using different cost algorithm. In addition, by introducing the total margin algorithm to replace the conventional soft margin algorithm, a lower generalization error bound can be obtained. Those three functions are embodied into the traditional SVM so that the TAF-SVM is proposed and reformulated in both linear and nonlinear cases. By using two databases, the Chung Yuan Christian University (CYCU) multiview and the facial recognition technology (FERET) face databases, and using the kernel Fisher's discriminant analysis (KFDA) algorithm to extract discriminating face features, experimental results show that the proposed TAF-SVM is superior to SVM in terms of the face-recognition accuracy. The results also indicate that the proposed TAF-SVM can achieve smaller error variances than SVM over a number of tests such that better recognition stability can be obtained