Support Vector Machine (SVM) is a state-of-the-art classification method
widely used in science and engineering due to its high accuracy, its ability to
deal with high dimensional data, and its flexibility in modeling diverse
sources of data. In this paper, we propose an autotuning-based optimization
framework to quantify the ranges of hyperparameters in SVMs to identify their
optimal choices, and apply the framework to two SVMs with the mixed-kernel
between Sigmoid and Gaussian kernels for smart pixel datasets in high energy
physics (HEP) and mixed-kernel heterojunction transistors (MKH). Our
experimental results show that the optimal selection of hyperparameters in the
SVMs and the kernels greatly varies for different applications and datasets,
and choosing their optimal choices is critical for a high classification
accuracy of the mixed kernel SVMs. Uninformed choices of hyperparameters C and
coef0 in the mixed-kernel SVMs result in severely low accuracy, and the
proposed framework effectively quantifies the proper ranges for the
hyperparameters in the SVMs to identify their optimal choices to achieve the
highest accuracy 94.6\% for the HEP application and the highest average
accuracy 97.2\% with far less tuning time for the MKH application.

支持向量机 (SVM) 是一种科学和工程中广泛使用的最先进的分类方法，本文提出了一种基于自动调节优化框架的方法来量化 SVM 中超参数的范围，以确定它们的最佳选择，并将该框架应用于混合核函数的智能像素数据集和混合核异质结晶体管 (MKH) 数据集，实验结果表明超参数的最佳选择对于不同的应用和数据集有很大的差异，未知的超参数 C 和 coef0 会导致严重的低准确率，该方法可以有效地量化超参数的合适范围并确定其最佳选择来实现最高准确率，高能物理 (HEP) 应用的最高准确率达到 94.6％，MKH 应用的平均准确率为 97.2％且调优时间大大减少。