论文标题
物理分析的异常检测，而不是监督学习
Anomaly Detection for Physics Analysis and Less than Supervised Learning
论文作者
论文摘要
现代的机器学习工具提供了令人兴奋的可能性，可以定性地改变新粒子搜索的范例。特别是，新方法可以通过直接从数据中学习来获得对无法预见的方案的敏感性来扩展搜索程序。新想法的显着增长，它们才刚刚开始应用于实验数据。本章介绍了这些新的异常检测方法，范围从完全监督的算法到无监督，并包括弱监督的方法。
Modern machine learning tools offer exciting possibilities to qualitatively change the paradigm for new particle searches. In particular, new methods can broaden the search program by gaining sensitivity to unforeseen scenarios by learning directly from data. There has been a significant growth in new ideas and they are just starting to be applied to experimental data. This chapter introduces these new anomaly detection methods, which range from fully supervised algorithms to unsupervised, and include weakly supervised methods.
