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In this paper we give a proof of the universality of the Cellular Neural Network - Universal Machine (CNN-UM) alternative to those presented so far. On the one hand, this allows to find a general structure for CNN-UM programs; on the other hand, it helps to formally demonstrate that machine learning techniques can be used to find CNN-UM programs automatically. Finally, we report on two experiments...
Ranking support vector machine (RSVM) learning is equivalent to solving a convex quadratic programming problem. Currently there exists some difficulties for exact online ranking learning. This paper presents an exact and effective method that can solve the online ranking learning problem and shows the feasibility and finite convergence of the algorithm from the perspective of theoretical analysis...
Today, decomposition methods are one of the most popular methods for training support vector machines (SVMs). With the use of kernels that do not satisfy Mercer's condition, new techniques must be designed to handle nonpositive-semidefinite kernels resulting to this choice. In this work we incorporate difference of convex (DC functions) optimization techniques into decomposition methods to tackle...
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