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In object classification, feature combination can usually be used to combine the strength of multiple complementary features and produce better classification results than any single one. While multiple kernel learning (MKL) is a popular approach to feature combination in object classification, it does not always perform well in practical applications. On one hand, the optimization process in MKL...
Classifier fusion methods are usually used to combine multiple classification decisions and generate better classification results than any single classifier. In order to improve object classification accuracy, it is a common method to assign weights to classifiers based on their importance in a multiple decision system. In this paper we put forward a method to weight different classifiers in classifier...
Classifier combination can be used to combine multiple classification decisions to improve object classification performance, and weighted average is a popular method for this purpose. In this paper we propose to use a graph-theoretic clustering method to define the weights for SVM classifier decisions. Specifically, we use the dominant set clustering to evaluate the difficulty of a kernel matrix...
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