The symmetric eigenvalue problem
The symmetric eigenvalue problem
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Localized twin SVM via convex minimization
Neurocomputing
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In this paper, we propose a fuzzy extension to proximal support vector classification via generalized eigenvalues. Here, a fuzzy membership value is assigned to each pattern, and points are classified by assigning them to the nearest of two non parallel planes that are close to their respective classes. The algorithm is simple as the solution requires solving a generalized eigenvalue problem as compared to SVMs, where the classifier is obtained by solving a quadratic programming problem. The approach can be used to obtain an improved classification when one has an estimate of the fuzziness of samples in either class.