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- W2393969781 abstract "To the problems that decision directed acylic graph support vector machines(DDAGSVMs) suffer from training a mass of SVMs and the error accumulation effect,a hybrid multi-class classification algorithm taking optimized linear discriminant analysis(LDA) and SVM as node classifiers is proposed.According to the characteristics of projecting high-dimension data to low-dimensional space,an optimized LDA classification threshold is derived.Then the linear separability of each pair of classes is defined with repect to the classification accuracy of the optimized LDA.The proposed algorithm trains SVMs for the dichotomies with relatively low linear separability,and then updates the separability matrix with classification accuracy of the SVMs.In the classification phase,the separability matrix is employed to decide the decision route of the DDAG.Finally,experiments show that,comparing with DDAGSVM,the proposed algorithm possesses higher training and classifying speed without the loss of generalization accuracy." @default.
- W2393969781 created "2016-06-24" @default.
- W2393969781 creator A5080670566 @default.
- W2393969781 date "2009-01-01" @default.
- W2393969781 modified "2023-09-24" @default.
- W2393969781 title "Hybrid multi-class classification approach based on LDA and SVM" @default.
- W2393969781 hasPublicationYear "2009" @default.
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