# Quantitative Classification Model of Composite Product Image Based on Event-Related Potential
## 4 SVM-Based Classification Model of Semantic Matching between Physiological Data and Image
### SVM Kernel Function Selection and Parameter Optimization  
Linear kernel function, polynomial kernel function, Gaussian kernel function and sigmoid kernel function are selected for the preliminary test, 70% of the samples are used as a training set and 30% as a test set, the results are shown in Table 2.  
It can be seen from the table that the Gaussian kernel function has a high accuracy rate. Therefore, the further parameter adjustment for the Gaussian kernel function is mainly to adjust the penalty coefficient C and the gamma value. For Gaussian kernel function, gamma is one of its own parameters, and its expression is: gamma = \(\frac{1}{2\sigma^{2}}\), the gamma value is inversely proportional to the \(\sigma\) value in the expression, which reflects the balance between the generalization ability of the model and the accuracy of the model. If the \(\sigma\) value is too small, the relationship between the features will become smaller, the learning process will be more complicated, and the generalization ability will correspondingly decrease. If the \(\sigma\) value is too large, various features will affect each other, and the accuracy of the model will decrease. The exhaustion method was used to estimate the gamma value parameters and draw the learning curve, as shown in Figure 12. As shown in Figure 12a, the range of gamma value is \(\left[10^{-10},\ 10\right]\), the step length is 50, the accuracy rate is 93.3%, gamma = 0.752. In Figure 12b, the range of gamma value is extended to \(\left[10^{-10},\ 100\right]\), and the step size is 50. The accuracy is 92.78% and gamma = 0.625, and the accuracy becomes stable after the gamma value exceeds 20, so it is unnecessary to extend the gamma value. In Figure 12c, the gamma value range is restored to \(\left[10^{-10},\ 10\right]\) with 100 steps, and the accuracy is 92.78%, gamma = 0.599. Therefore, it can be seen gamma that 0.752 is appropriate. In the determination, the gamma after the value was 0.752, the \(C\) values were enumerated and a learning curve was drawn. As shown in Figure 13a, the \(C\) value range was \(\left[0.01,30\right]\), the step size was 50, the accuracy rate was 94.44%, and \(C\) = 1.846; as shown in Figure 13b for \(C\), the value range was narrowed to \(\left[0.01,\ 3\right]\), the step size was 100, the accuracy can be 94.44%, and \(C\) = 1.263. \begin{table}
\begin{tabular}{c c c c} \hline
**Characteristic No.** & **Stage** & **Electrode Position** & **Intercept Period** & **Component** \\ \hline
1 & Start-up phase & Average value of AF3 and F7 & 200–400 ms & P300 \\
2 & Start-up phase & Mean value of O1 and P3 & 200–400 ms & P300 \\
3 & Start-up phase & Average value of AF3 and F7 & 500–700 ms & LPP \\
4 & Start-up phase & Mean value of T7 and P5 & 500–700 ms & LPP \\
5 & Start-up phase & Average value of AF4 and F8 & 300–500 ms & N400 \\
6 & Target phase & Mean value of O1 and P3 & 200–400 ms & P300 \\
7 & Target phase & Mean value of O2 and P4 & 200–400 ms & P300 \\
8 & Target phase & Average value of AF3 and F7 & 300–500 ms & N400 \\
9 & Target phase & Average value of AF4 and F8 & 300–500 ms & N400 \\
10 & Target phase & Mean value of F1 and FC5 & 300–500 ms & N400 \\
11 & Target phase & Mean value of F2 and FC6 & 300–500 ms & N400 \\
12 & Target phase reaction time & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Model characteristic variables. \begin{table}
\begin{tabular}{c c} \hline
**Kernel Function Name** & **Accuracy** \\ \hline Linear kernel & 76.11\% \\ Polynomial kernel & 70.55\% \\ Gaussian kernel & 86.67\% \\ Sigmoid core function & 65\% \\ \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy of four types of kernel functions (gamma = “auto”, linear kernel degree = 1, C = 1). In summary, we can know from the drawing of the learning curve gamma that the value is 0.752. If the \(C\) value is 1.263, the accuracy can reach 94.44%. Now the results are checked using the grid search method. The grid search method is also an algorithm to find the optimal combination of parameters using means of exhaustion.