Weakly supervised classification in high energy physics

Article, Preprint English OPEN
Dery, Lucio; Nachman, Benjamin; Rubbo, Francesco; Schwartzman, Ariel;
(2017)
  • Publisher: Springer/SISSA
  • Journal: JHEP (issn: 1029-8479)
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.1007/JHEP05(2017)145
  • Subject: QC770-798 | High Energy Physics - Phenomenology | Physics - Data Analysis, Statistics and Probability | Statistics - Machine Learning | Nuclear and particle physics. Atomic energy. Radioactivity | Jets
    arxiv: Computer Science::Machine Learning
    acm: ComputingMethodologies_PATTERNRECOGNITION

Abstract As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are t... View more
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