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The Large Margin Nearest Neighbor (LMNN) metric learning algorithm has been successfully used in many applications and continuously motivates new research works. However, the high computational complexity of training the LMNN algorithm inherent from the k-Nearest Neighbor (kNN) search makes it inapplicable to large datasets, especially when we need to tune the hyper-parameters of the LMNN algorithm...
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-the-art performance. A general drawback of these strategies is the high computational cost during training, that prevents their application to large-scale problems. They also do not provide theoretical guarantees on their convergence rate...
The fast SVM classification algorithm - FCSVM adopt the way to transform of support vector sets, substituting a subset of all the support vector classification for support vector machines, it makes the classification speed improve a lot compared with traditional SVM algorithm under the premise of not losing. In order to obtain the minimum subset of support vector, and to avoid the movement of support...
We develop a simple and fast (1 + epsiv)-approximate algorithm for computing the minimum enclosing ball (MEB) of a points set in high dimensional Euclidean space without requirement of any numerical solver. We prove theoretically that the proposed simpler minimum enclosing ball (SMEB) algorithm converges to the optimum within any precision in O(1/epsiv) iterations. Compared to the MEB algorithms adopted...
We report our work on the algorithmic development of an evolutionary methodology for automatic configuration of metaheuristic algorithms for solving complex combinatorial optimization problems. We term it automatic configuration engine for metaheuristics (ACEM). We first propose a novel left variation-right property (LVRP) tree structure to manage various metaheuristic procedures and properties. With...
Support vector machine (SVM) has become a popular classification tool but one of its disadvantages is large memory requirement and computation time when dealing with large datasets. Parallel methods have been proposed to speed up the process of training SVM. An improved cascade SVM training algorithm is proposed, in which multiple SVM classifiers are applied. The support vectors are obtained by feeding...
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