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Certain equations are found to be independent of classification effects.
Table 3 Comparison of the classification effects of the four kernel functions Categories of function Iterations Minimum value by quadratic programming Decision of function bias b nSV nBSV Linear function 205 −20.090207 3.858766 35 21 Polynomial function 34 −58.834062 59.810486 62 59 Radial basis function 48 −45.700202 −0.200129 62 54 Sigmoid kernel function 35 −52.498549 0.482414 62588.
No other classification effects were significant in this ROI.
Neither of the univariate effects overlapped with the classification effects.
An ANOVA constructs tests to determine the significance of the classification effects.
Good classification effects can be obtained with only a few learning samples.
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Its classification effect works better than that of LSTM.
The classification effect is more accurate, and an accuracy rate over 90% can be reached.
The method is for each classification of continuous cumulative values, and it has a good classification effect.
According to the figure, we can select an optimal pattern recognition algorithm to achieve the optimal classification effect.
After clustering analysis and adjusting the penalty factor, the classification effect of the modified SVM model is compared with the standard SVM model, as shown in Table 2.
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