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- W2034156072 abstract "This paper presents a two-level stacked generalization scheme composed of three generalizers of support vector machines (SVMs) for image classification. They are color, texture, and high-level concept SVMs. The focus of this paper is to investigate two training strategies based on two-fold cross-validation and non-cross-validation for the proposed classification scheme by evaluating their classification performances, margin of the hyperplane and numbers of support vectors of SVMs. The results show that the non-cross-validation training method performs better, having higher correct classification rates, larger margin of the hyperplane, and smaller numbers of support vectors." @default.
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- W2034156072 date "2005-03-01" @default.
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- W2034156072 title "Training support vector machines based on stacked generalization for image classification" @default.
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- W2034156072 doi "https://doi.org/10.1016/j.neucom.2004.08.005" @default.
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