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We propose twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization.(More)
Traditional support vector machines (SVMs) assign data points to one of two classes, represented in the pattern space by two disjoint half-spaces. In this paper, we propose a fuzzy extension to proximal SVMs, where a fuzzy membership is assigned to each pattern, and points are classi1ed by assigning them to the nearest of two parallel planes that are kept(More)
In the most basic application of Ant Colony Optimization (ACO), a set of artificial ants find the shortest path between a source and a destination. Ants deposit pheromone on paths they take, preferring paths that have more pheromone on them. Since shorter paths are traversed faster, more pheromone accumulates on them in a given time, attracting more ants(More)
A dual for linear programming problems with fuzzy parameters is introduced and it is shown that a two person zero sum matrix game with fuzzy pay-oos is equivalent to a primal–dual pair of such fuzzy linear programming problems. Further certain diiculties with similar studies reported in the literature are discussed.
Kumar et al. (2006) obtained a fifth order polynomial in ω for the dispersion relation and pointed out that the calculations preformed by Porter et al. (1994) and by Dwivedi & Pandey (2003) seem to be in error, as they obtained a sixth order polynomial. The energy equation of Dwivedi & Pandey (2003) was dimensionally wrong. Dwivedi & Pandey (2006) corrected(More)