论文标题
使用强化学习和多维贝叶斯分类的人工神经网络的比较研究，使用parzen密度估计，以鉴定部分甲基化乙醇的GC-EIMS光谱
A Comparative study of Artificial Neural Networks Using Reinforcement learning and Multidimensional Bayesian Classification Using Parzen Density Estimation for Identification of GC-EIMS Spectra of Partially Methylated Alditol Acetates
论文作者
论文摘要
这项研究报告了针对全球基于Web的气相色谱电子影响质谱（GC-EIMS）的图案识别搜索引擎的开发，该数据库的部分甲基化乙醇酸乙酸酯（PMAA）。在这里，我们还报告了本研究使用的两种模式识别技术的比较结果。第一种技术是使用贝叶斯分类器和parzen密度估计器的统计技术。第二种技术涉及通过增强学习训练的人工神经网络模块。我们在这里证明，这两个系统在识别少量噪声的光谱方面表现良好。两种系统的性能都随信噪比（SNR）的降解而降低。在处理部分光谱（缺少数据）时，人工神经网络系统的表现更好。开发的系统是在万维网上实现的，旨在使用在任何GC-EIMS仪器上记录的这些分子的谱图识别PMAA。因此，该系统对GC-EIMS光谱中的仪器和柱依赖性变化不敏感。
This study reports the development of a pattern recognition search engine for a World Wide Web-based database of gas chromatography-electron impact mass spectra (GC-EIMS) of partially methylated Alditol Acetates (PMAAs). Here, we also report comparative results for two pattern recognition techniques that were employed for this study. The first technique is a statistical technique using Bayesian classifiers and Parzen density estimators. The second technique involves an artificial neural network module trained with reinforcement learning. We demonstrate here that both systems perform well in identifying spectra with small amounts of noise. Both system's performance degrades with degrading signal-to-noise ratio (SNR). When dealing with partial spectra (missing data), the artificial neural network system performs better. The developed system is implemented on the world wide web, and is intended to identify PMAAs using submitted spectra of these molecules recorded on any GC-EIMS instrument. The system, therefore, is insensitive to instrument and column dependent variations in GC-EIMS spectra.
