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The Support vector machines derive the class decision hyper planes from a few, selected prototypes, the support vectors (SVs) according to the principle of structure risk minimization, so they have good generalization ability. We proposed a new prototype selection method based on support vectors for nearest neighbor rules. It selects prototypes only from(More)
Data fusion strategies based on multi-class support vector machines are proposed. In the centralized scheme, the information from several sources is combined to construct an input space. In the distributed schemes, the input space is constructed corresponding to each information source and the multi-class support vector machine is used for modeling each(More)
To deal with multi-source multi-class problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Dempster-Shafer evidence theory is proposed. The MPSVM is designed by mapping the outputs of standard support vector machines into a calibrated posterior probability using a learned sigmoid function, and then(More)
The method of attribute reduction is applied to medical diagnostic decision by combining basic theory of support vector machines for nonlinear classification. Decision-making performance of the proposed method is compared with that of the conventional methods. The results indicate our method can decrease the computation complexity and memory requirement a(More)
How to extend standard support vector machines to solve multi-class classification problem and yield the outputs in the frame of Dempster-Shafer theory is useful. The multi-class probability support vector machine is proposed, firstly. The Dempster-Shafer theory based multi-class support vector machine is designed by constructing probability support vector(More)
We designed a global optimization algorithm and a local optimization algorithm based on immune optimization, and then proposed an effective self-tuning scheme of fuzzy controller for time-variable plant. For a time-variable plant, the global optimization algorithm was adopted to optimize nominal controller offline. When the plant changed, new optimal(More)