Use of random time-intervals (RTIs) generation for biometric verification
Pattern Recognition
On minimum class locality preserving variance support vector machine
Pattern Recognition
Matrix pattern based minimum within-class scatter support vector machines
Applied Soft Computing
Twin Mahalanobis distance-based support vector machines for pattern recognition
Information Sciences: an International Journal
On minimum distribution discrepancy support vector machine for domain adaptation
Pattern Recognition
Journal of Medical Systems
Higher rank Support Tensor Machines for visual recognition
Pattern Recognition
Discriminant subspace learning based on support vectors machines
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Using robust dispersion estimation in support vector machines
Pattern Recognition
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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.