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- W2147245425 abstract "In this paper, an electronic nose data classification approach based on least square support vector machines (LS-SVM) in combination with principal component analysis (PCA) is investigated. The electronic nose data are first converted into PCA, where the data are projected from a high dimensional space into a low dimensional space, preferably two or three dimensions. Then the resulting features from the PCA are sent into the LS-SVM classifier in order to recognize the gas category. The performance of the proposed approach is validated by cross-validation technique. An experiment has been demonstrated by using coffee data from different types of coffee blends. Experimental results show that the LS-SVM in combination with PCA is an effective technique for the classification of electronic nose data." @default.
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- W2147245425 date "2009-12-01" @default.
- W2147245425 modified "2023-09-26" @default.
- W2147245425 title "Least Square Support Vector Machines in Combination with Principal Component Analysis for Electronic Nose Data Classification" @default.
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- W2147245425 doi "https://doi.org/10.1109/isise.2009.138" @default.
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