Support Vector Machines and Generalisation in HEP
This work addresses the overlooked problem of generalization in multivariate algorithms for particle physics analyses, though it is incremental as it builds on existing SVM and cross-validation methods.
The paper reviews support vector machines (SVMs) and their application in High Energy Physics, highlighting their reduced susceptibility to overfitting compared to neural networks and decision trees, and introduces improvements to SVM functionality and cross-validation tools within the TMVA framework.
We review the concept of support vector machines (SVMs) and discuss examples of their use. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be less susceptible to over fitting than those other algorithms are to over training. This issue is related to the generalisation of a multivariate algorithm (MVA); a problem that has often been overlooked in particle physics. We discuss cross validation and how this can be used to improve the generalisation of a MVA in the context of High Energy Physics analyses. The examples presented use the Toolkit for Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the SVM functionality and new tools introduced for cross validation within this framework.