Zusammenfassung
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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