一种基于自动调参的混合核SVM分类优化框架及其在智能像素数据集与异质结晶体管中的应用
An Autotuning-based Optimization Framework for Mixed-kernel SVM Classifications in Smart Pixel Datasets and Heterojunction Transistors
AI总结：
本文提出一种基于自动调参的优化框架，用于量化混合核SVM超参数的最优范围，并在高能物理智能像素数据集和混合核异质结晶体管应用中分别取得94.6%和97.2%的高分类精度。
摘要：
支持向量机（SVM）是一种最先进的分类方法，因其高精度、处理高维数据的能力以及对多样化数据源建模的灵活性而被广泛应用于科学与工程领域。本文提出了一种基于自动调参的优化框架，用于量化SVM中超参数的范围以确定其最优选择，并将该框架应用于两个采用Sigmoid核与高斯核混合核的SVM：一个用于高能物理（HEP）中的智能像素数据集，另一个用于混合核异质结晶体管（MKH）。实验结果表明，SVM及核函数中超参数的最优选择在不同应用和数据集之间存在很大差异，选择最优超参数对于混合核SVM获得高分类精度至关重要。在混合核SVM中，对超参数C和coef0的无依据选择会导致精度严重偏低，而所提出的框架能够有效量化SVM中超参数的合适范围，从而确定其最优选择，在HEP应用中达到最高精度94.6%，在MKH应用中达到最高平均精度97.2%，且调参时间大幅减少。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
