dorsal/arxiv
View SchemaPhysicsGP: A Genetic Programming Approach to Event Selection
| Authors | Kyle Cranmer, R. Sean Bowman |
|---|---|
| Categories | |
| ArXiv ID | physics/0402030 |
| URL | https://arxiv.org/abs/physics/0402030 |
| DOI | 10.1016/j.cpc.2004.12.006 |
Abstract
We present a novel multivariate classification technique based on Genetic Programming. The technique is distinct from Genetic Algorithms and offers several advantages compared to Neural Networks and Support Vector Machines. The technique optimizes a set of human-readable classifiers with respect to some user-defined performance measure. We calculate the Vapnik-Chervonenkis dimension of this class of learning machines and consider a practical example: the search for the Standard Model Higgs Boson at the LHC. The resulting classifier is very fast to evaluate, human-readable, and easily portable. The software may be downloaded at: http://cern.ch/~cranmer/PhysicsGP.html
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"abstract": "We present a novel multivariate classification technique based on Genetic\nProgramming. The technique is distinct from Genetic Algorithms and offers\nseveral advantages compared to Neural Networks and Support Vector Machines. The\ntechnique optimizes a set of human-readable classifiers with respect to some\nuser-defined performance measure. We calculate the Vapnik-Chervonenkis\ndimension of this class of learning machines and consider a practical example:\nthe search for the Standard Model Higgs Boson at the LHC. The resulting\nclassifier is very fast to evaluate, human-readable, and easily portable. The\nsoftware may be downloaded at: http://cern.ch/~cranmer/PhysicsGP.html",
"arxiv_id": "physics/0402030",
"authors": [
"Kyle Cranmer",
"R. Sean Bowman"
],
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"physics.data-an"
],
"doi": "10.1016/j.cpc.2004.12.006",
"title": "PhysicsGP: A Genetic Programming Approach to Event Selection",
"url": "https://arxiv.org/abs/physics/0402030"
},
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