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A sequential randomized algorithm is developed for robust optimization which is to minimize a linear objective function subject to a parameter dependent convex constraint for all uncertain parameter values. The algorithm is realized as a probabilistic cutting plane technique based on maximum volume ellipsoid center, where candidates of the optimal value and of the optimal solution are sequentially...
This paper presents a probabilistic cutting plane technique for solving a robust feasibility problem which is to find a solution satisfying a parameter-dependent convex constraint for all possible parameter values. The proposed algorithm employs random samples of the parameter and maximum volume ellipsoid centers of candidates of the solution set. It is shown that the numbers of updates and random...
This paper gives an overview on probabilistic approach to robust optimization and chance constrained optimization. The problems are to minimize a linear objective function subject to a parameter dependent convex constraint, where a probability measure is introduced onto the parameter set. Two randomized techniques, the scenario optimization and the sequential optimization, are summarized, where characteristics...
Hua et al. have proposed a stable and efficient tracking algorithm called ldquoK-means trackerrdquo[2, 3, 5]. This paper describes an adaptive non-target cluster center selection method that replaces the one used in k-means tracker where non-target cluster center are selected at fixed interval. Non-target cluster centers are selected from the ellipse that defines the area for searching the target...
This work presents an acoustic model adaptation method for speaker verification (SV) in environments with additive noise. In contrast to traditional acoustic model adaptation techniques that adapt the models parameters based on a model of the noise, acoustic model enhancement (AME) belongs to a new scheme in which the models are adapted to the speech enhancement strategy. The theoretical framework...
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