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- W2352504298 abstract "A new classification algorithm based on support vector machine (SVM) theory and principal component analysis (PCA) techniques is presented. Noise is eliminated by PCA to increases the predicted classification accuracy. When training sets with uneven class sizes are used, the result is undesirably biased towards the larger class. The cause of this effect and the compensation method are proposed in this paper. Numerical experiments for classifying operation state of wastewater treatment processes show that the proposed algorithm can be used to obtain better classification accuracy than the original SVM." @default.
- W2352504298 created "2016-06-24" @default.
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- W2352504298 date "2004-01-01" @default.
- W2352504298 modified "2023-09-25" @default.
- W2352504298 title "Noise-immune Weighted SVM Classification Algorithm" @default.
- W2352504298 hasPublicationYear "2004" @default.
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