# Multi-Angle Fast Neural Tangent Kernel Classifier
## 6 Discussion  
The SVM model of multi-kernel learning is introduced, and NTK is used as the base kernel function of multi-kernel learning. More than twenty UCI datasets are selected, and a multi-kernel-NTK diagnostic model using HMPSO optimization is built for multi-domain classification tasks, which extracts features of sample data from multifaceted high-dimensional space and performs linear convex combination, which can better identify sample data and expand the search of the model space. At the same time, the multiscale multi-kernel learning method is used to construct 20 multiscale RBF kernel functions and MDK kernel functions to create the RBF-Boolean model and the POLY-Boolean model with a linear combination of POLY and MDK. The above models are compared with RF and Adaboost models, and the superiority of multi-kernel. The power of the NTK model is demonstrated by statistical tests. We constructed the Block-NTK model from the perspective of sample size, using the idea of data cutting, dividing the original data into several subsets of the same size, ensuring that each part of the subset has data of all categories, training on each subset in parallel using the NTK classifier, and aggregating the training results of different subsets. As can be seen from Table 4, the accuracy of our proposed NTK improvement model outperforms the commonly used classification models in most multi-domain, different-size datasets. In Table 5, we show the relative time complexity of different models running different datasets and we mark the model with the shortest running time in each dataset as 1. The higher relative complexity of the model represents its higher time complexity. Table 5 shows that the decision tree model achieves the fastest speed in most datasets. Still, our proposed model is second only to the decision tree model in terms of average running rate while maintaining the best average classification accuracy. Figure 5: Effect of penalty parameter C on the accuracy of the model. The acceleration performance and robustness of the multi-kernel-NTK model are investigated. It is found that compared with the traditional NTK model, the multi-kernel-NTK model achieves excellent acceleration performance in most datasets, saving up to nearly 60% of the running time and improving the model accuracy, ranging from 1% to 25% in different datasets. Multi-kernel-NTK reduces the sensitivity of the deep NTK kernel to parameters and penalty parameters and improves the robustness of the model. When L is more significant than four, the NTK model accuracy gradually decreases as the number of network layers increases. At the same time, multi-kernel-NTK has better resistance to this phenomenon, and when 4 < L < 13, multi-kernel-NTK performs better in all data. The average accuracy of multi-kernel-NTK in all datasets is improved by 21.6% compared with NTK. The training time of the NTK model increases exponentially with the increase in C. Multi-kernel-NTK model can alleviate this trend well; when 2 < C < 128, the average time consumed by multi-kernel-NTK in all datasets is reduced by 93.6% compared with NTK. The average accuracy of multi-kernel-NTK in all datasets is improved by 8.3% compared with NTK when 0.04 < C < 128.