人工神经网络和Boosted决策树的稳定性和鲁棒性研究
Studies of Stability and Robustness for Artificial Neural Networks and Boosted Decision Trees
摘要：
在本文中，我们比较了性能，稳定性和鲁棒性的人工神经网络（ANN）和提升决策树（BDT）使用MiniBooNE蒙特卡罗样本。这些方法试图在给定多个识别变量的情况下对事件进行分类。我们在以前的出版物中讨论了BDT算法。本文通过对测试样本的输入变量进行涂抹和移位来进行测试。基于这些研究，BDT具有更好的颗粒识别性能比人工神经网络，通过移动或涂抹测试结果的变量得到的分类的退化是较小的BDT比人工神经网络。
英文摘要：
In this paper, we compare the performance, stability and robustness of Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT) using MiniBooNE Monte Carlo samples. These methods attempt to classify events given a number of identification variables. The BDT algorithm has been discussed by us in previous publications. Testing is done in this paper by smearing and shifting the input variables of testing samples. Based on these studies, BDT has better particle identification performance than ANN. The degradation of the classifications obtained by shifting or smearing variables of testing results is smaller for BDT than for ANN.
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。
