支持向量机及其在HEP中的推广
Support Vector Machines and generalisation in HEP
摘要：
我们回顾了支持向量机（SVM）的概念，并讨论了它们在许多情况下使用的例子。几个SVM的实现已被用于HEP，我们使用工具包的多变量分析（TMVA）的实现来验证该算法。我们讨论的例子相关的HEP，包括背景抑制$H\toτ^+τ^-$在大型强子对撞机与几个不同的核函数。性能基准测试导致超参数选择的泛化问题。避免MVA超参数优化中的微调（过度训练或过度拟合），即确保MVA的一般性能独立于训练，验证和测试样本的能力，是至关重要的。我们讨论了这个问题，并比较和对比性能的保持和k折交叉验证。我们已经扩展了SVM的功能，并引入了工具，以方便在TMVA交叉验证，并根据这些改进目前的结果。
英文摘要：
We review the concept of Support Vector Machines (SVMs) and discuss examples of their use in a number of scenarios. Several SVM implementations have been used in HEP and we exemplify this algorithm using the Toolkit for Multivariate Analysis (TMVA) implementation. We discuss examples relevant to HEP including background suppression for $H\toτ^+τ^-$ at the LHC with several different kernel functions. Performance benchmarking leads to the issue of generalisation of hyper-parameter selection. The avoidance of fine tuning (over training or over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure generalised performance of an MVA that is independent of the training, validation and test samples, is of utmost importance. We discuss this issue and compare and contrast performance of hold-out and k-fold cross-validation. We have extended the SVM functionality and introduced tools to facilitate cross validation in TMVA and present results based on these improvements.
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。
