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SVM Applied to Trace Element Analysis of Tea

The quality of mathematical model made by SVC is dependent on the selection of kernel function and parameter C in computation. In order to find the best choice of kernel function and the value of parameter C, the rate of correctness (P ) of the prediction in LOO cross-validation test is used as the criterion for this selection work. [Pg.227]

By using SVC method, it has been found that these three kinds of tea can be classified with linear kernel functions, with the rate of correctness of prediction equal to 100% by using C = 10. By this way, the criterion of samples of oolong tea can be found as follows  [Pg.228]

And the criterion to differentiate green tea of southeastern China from black tea of south China is as follows  [Pg.228]

The classification by Fisher method is also clear-cut, but both the result of LOO cross-validation test of Fisher method and the results of K-Nearest Neighbor (KNN) methods (k=l, 3 or 5) lead to some misclassification, while the result of the LOO cross-validation test of SVC (linear kernel, C =10) gives 100% rate of correctness. [Pg.228]


See other pages where SVM Applied to Trace Element Analysis of Tea is mentioned: [Pg.226]   


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