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 optimises 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.
replaces oai:arXiv.org:physics/0402030
http://www.elsevier.com/wps/find/journaldescription.cws_home/505710/description#description
p171 "It is meaningless to calculate the VCD
(Vapnik-Chervonenkis dimension) for GP in general..."
"by placing a bound on either size... or the degree
of the polynomial, we can calculate a sensible
VCD."
GP compared with ANN (backprop + momentum) and SVM with
RBF kernel (BSVM-2.0). Training data subsampled.
p174 "GP approach does not seem particularly
sensitive to the size penalty of mutation rates".
%0 Journal Article
%1 cranmer:2005:CPC
%A Cranmer, Kyle
%A Bowman, R. Sean
%D 2005
%J Computer Physics Communications
%K Classification, Genetic Neural Support Triggering, VC algorithms, dimension, genetic machines networks, programming, vector
%N 3
%P 165--176
%R doi:10.1016/j.cpc.2004.12.006
%T PhysicsGP: A Genetic Programming approach to event
selection
%V 167
%X 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 optimises 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.
@article{cranmer:2005:CPC,
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 optimises 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.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Cranmer, Kyle and Bowman, R. Sean},
biburl = {https://www.bibsonomy.org/bibtex/22147cbfd001c12ba19943d268baa57e3/brazovayeye},
doi = {doi:10.1016/j.cpc.2004.12.006},
interhash = {28a3f537edd229b97c04ab501d81537b},
intrahash = {2147cbfd001c12ba19943d268baa57e3},
issn = {0010-4655},
journal = {Computer Physics Communications},
keywords = {Classification, Genetic Neural Support Triggering, VC algorithms, dimension, genetic machines networks, programming, vector},
month = {1 May},
notes = {replaces oai:arXiv.org:physics/0402030
http://www.elsevier.com/wps/find/journaldescription.cws_home/505710/description#description
p171 {"}It is meaningless to calculate the VCD
(Vapnik-Chervonenkis dimension) for GP in general...{"}
{"}by placing a bound on either size... or the degree
of the polynomial, we can calculate a sensible
VCD.{"}
GP compared with ANN (backprop + momentum) and SVM with
RBF kernel (BSVM-2.0). Training data subsampled.
p174 {"}GP approach does not seem particularly
sensitive to the size penalty of mutation rates{"}.},
number = 3,
pages = {165--176},
timestamp = {2008-06-19T17:38:13.000+0200},
title = {{PhysicsGP}: {A} Genetic Programming approach to event
selection},
volume = 167,
year = 2005
}