Speaker
Dr
Fredrik Tegenfeldt
(Iowa State University)
Description
In high-energy physics, with the search for ever
smaller signals in ever larger data sets, it has
become essential to extract a maximum of the
available information from the data. Multivariate
classification methods based on machine learning
techniques have become a fundamental ingredient to
most analyses. Also the multivariate classifiers
themselves have significantly evolved in recent
years. Statisticians have found new ways to tune
and to combine classifiers to further gain in
performance. Integrated into the analysis
framework ROOT, TMVA is a toolkit which holds a
large variety of multivariate classification
algorithms. They range from rectangular cut
optimization using a genetic algorithm and from
likelihood estimators, over linear discriminants
and non-linear neural networks, to sophisticated
more recent classifiers such as boosted decision
trees, rule ensemble fitting and a support vector
machine. TMVA manages the simultaneous training,
testing, and performance evaluation of all these
classifiers with a user-friendly interface, and
expedites the application of the trained
classifiers to data.
Authors
Dr
Andreas Hoecker
(CERN)
Dr
Fredrik Tegenfeldt
(Iowa State University)
Dr
Helge Voss
(MPI fur Kernphysik Heidelberg, Germany)
Dr
Jörg Stelzer
(CERN)
Dr
Kai Voss
(University of Victoria, Canada)
