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In this paper, a general kernelization framework for learning algorithms is proposed via a two-stage procedure, i.e., transforming data by kernel principal component analysis (KPCA), and then directly performing the learning algorithm with the transformed data. It is worth noting that although a very few learning algorithms were also kernelized by this procedure before, why and under what condition...
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