电子能量损失光谱的机器学习分类策略的比较:支持矢量机器和人工神经网络
Daniel Del-Pozo-Bueno1, Demie Kepaptsoglou2, Francesca Peiró1
1Departament d'Enginyeria Electrònica i Biomèdica, LENS-MIND, Universitat de Barcelona, Barcelona 08028, Spain; Institute of Nanoscience and Nanotechnology (IN2UB), Universitat de Barcelona, Barcelona 08028, Spain.
Ultramicroscopy
|August 9, 2023
概括
机器学习 (ML) 策略,包括支持矢量机 (SVM) 和人工神经网络 (ANN),增强了电子能量损失光谱 (EELS) 数据的分析. 对于SVM来说,一个新的cosine内核绕过了规范化,提高了准确性,并揭示了常见EELS数据处理中的偏差.
科学领域:
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
- 频谱学是一种光谱学.
背景情况:
- 机器学习 (ML) 对于分析扫描和传输电子显微镜 (S/TEM) 大数据集至关重要.
- 电子能量损失光谱 (EELS) 产生复杂的光谱数据,需要先进的分析方法.
- 分类EELS光谱对于材料的表征至关重要,例如确定氧化状态.
研究的目的:
- 评估和比较支持矢量机器 (SVM) 和人工神经网络 (ANN) 用于EELS光谱分类.
- 为软边际SVM引入和评估一种新型的等号核心.
- 调查光谱规范化对EELS.ML分类准确度的影响.
主要方法:
- 使用随机搜索和树结构的Parzen估计器对人工神经网络 (ANN) 配置进行系统分析.
- 为软边缘支向量机器 (SVM) 开发和应用一个新的等号核.
- 使用EELS数据评估ML分类器的性能,以确定过渡金属的氧化状态和分析能量损失转移.
主要成果:
- 对于SVM的新型共因内核,可以在没有先前光谱规范化的情况下准确地对EELS进行分类.
- 常见的规范化技术在EELS光谱中引入了显著的偏差,影响了分类.
- 在具有大量数据集的复杂分类任务中,ANN表现出卓越的性能,而SVM则适用于具有有限数据的更简单任务.
结论:
- 带有cosine内核的软边缘SVM为更简单的EELS分类任务提供了计算效率高的替代方案.
- 建议ANN用于复杂的EELS分类问题,需要广泛的培训数据.
- 了解规范化的影响对于使用ML进行可靠的EELS数据分析至关重要.
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