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Constructing accurate models that represent the underlying structure of Big Data is a costly process that usually constitutes a compromise between computation time and model accuracy. Methods addressing these issues often employ parallelisation to handle processing. Many of these methods target the Support Vector Machine (SVM) and provide a significant speed up over batch approaches. However, the...
With the appearance of large-scale database and people's increasing concern about individual privacy, privacy-preserving data mining becomes a hot study area, to which the support vector machine(SVM) belongs. In this paper, a novel privacy-preserving SVM for horizontally partitioned data is given. It has comparable accuracy to that of an ordinary SVM as we obtain the SVM by using the distinct property...
It is very important to construct the training set and determine the sample number in the regression problem. In this paper, a new idea of constructing the training set is elaborated. The key point of this idea is to choose the hyper-parameters before determining the training set. More importantly, a heuristic approach is proposed to select samples of support vector machine (SVM). Using these methods,...
A variety of flexible models have been proposed to detect objects in challenging real world scenes. Motivated by some of the most successful techniques, we propose a hierarchical multi-feature representation and automatically learn flexible hierarchical object models for a wide variety of object classes. To that end we not only rely on automatic selection of relevant individual features, but go beyond...
We investigate the use of support vector machines (SVMs) to determine simpler and better fit power macromodels of functional units for high-level power estimation. The basic approach is first to obtain the power consumption of the module for a large number of points in the input signal space. Least-squares SVMs are then used to compute the best model to fit this set of points. We have performed extensive...
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