New approaches for boosting to uniformity
The use of multivariate classifiers has become commonplace in particle physics. To enhance the performance, a series of classifiers is typically trained; this is a technique known as boosting. This paper explores several novel boosting methods that have been designed to produce a uniform selection e...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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IOP Publishing
2015
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| Online Access: | http://hdl.handle.net/1721.1/98474 https://orcid.org/0000-0001-8285-3346 |
| _version_ | 1826212569678872576 |
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| author | Rogozhnikov, A. Bukva, A. Gligorov, V. Ustyuzhanin, A. Williams, Michael |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Rogozhnikov, A. Bukva, A. Gligorov, V. Ustyuzhanin, A. Williams, Michael |
| author_sort | Rogozhnikov, A. |
| collection | MIT |
| description | The use of multivariate classifiers has become commonplace in particle physics. To enhance the performance, a series of classifiers is typically trained; this is a technique known as boosting. This paper explores several novel boosting methods that have been designed to produce a uniform selection efficiency in a chosen multivariate space. Such algorithms have a wide range of applications in particle physics, from producing uniform signal selection efficiency across a Dalitz-plot to avoiding the creation of false signal peaks in an invariant mass distribution when searching for new particles. |
| first_indexed | 2024-09-23T15:25:33Z |
| format | Article |
| id | mit-1721.1/98474 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:25:33Z |
| publishDate | 2015 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/984742022-09-29T14:40:31Z New approaches for boosting to uniformity Rogozhnikov, A. Bukva, A. Gligorov, V. Ustyuzhanin, A. Williams, Michael Massachusetts Institute of Technology. Department of Physics Williams, Michael The use of multivariate classifiers has become commonplace in particle physics. To enhance the performance, a series of classifiers is typically trained; this is a technique known as boosting. This paper explores several novel boosting methods that have been designed to produce a uniform selection efficiency in a chosen multivariate space. Such algorithms have a wide range of applications in particle physics, from producing uniform signal selection efficiency across a Dalitz-plot to avoiding the creation of false signal peaks in an invariant mass distribution when searching for new particles. National Science Foundation (U.S.) (Grant PHY-1306550) 2015-09-14T13:30:16Z 2015-09-14T13:30:16Z 2015-03 2014-12 Article http://purl.org/eprint/type/JournalArticle 1748-0221 http://hdl.handle.net/1721.1/98474 Rogozhnikov, A., A. Bukva, V. Gligorov, A. Ustyuzhanin, and M. Williams. “New Approaches for Boosting to Uniformity.” Journal of Instrumentation 10, no. 03 (March 1, 2015): T03002–T03002. © CERN 2015 https://orcid.org/0000-0001-8285-3346 en_US http://dx.doi.org/10.1088/1748-0221/10/03/t03002 Journal of Instrumentation Creative Commons Attribution http://creativecommons.org/licenses/by/3.0/ application/pdf IOP Publishing IOP |
| spellingShingle | Rogozhnikov, A. Bukva, A. Gligorov, V. Ustyuzhanin, A. Williams, Michael New approaches for boosting to uniformity |
| title | New approaches for boosting to uniformity |
| title_full | New approaches for boosting to uniformity |
| title_fullStr | New approaches for boosting to uniformity |
| title_full_unstemmed | New approaches for boosting to uniformity |
| title_short | New approaches for boosting to uniformity |
| title_sort | new approaches for boosting to uniformity |
| url | http://hdl.handle.net/1721.1/98474 https://orcid.org/0000-0001-8285-3346 |
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