Machine Learning Developments in ROOT
Paper in proceeding, 2017
ROOT is a software framework for large-scale data analysis that provides basic and advanced statistical methods used by high-energy physics experiments. It includes machine learning tools from the ROOT-integrated Toolkit for Multivariate Analysis (TMVA). We present several recent developments in TMVA, including a new modular design, new algorithms for pre-processing, cross-validation, hyperparameter-tuning, deep-learning and interfaces to other machine-learning software packages. TMVA is additionally integrated with Jupyter, making it accessible with a browser.
computer programming
Artificial intelligence
Author
A. Bagoly
Eötvös Loránd University (ELTE)
Adrian John Bevan
Queen Mary University of London
Andrew Carnes
University of Florida
Sergei Gleyzer
University of Florida
Lorenzo Moneta
CERN
A. Moudgil
IIT Hyderabad
Simon Pfreundschuh
Chalmers, Earth and Space Sciences, Global Environmental Measurements and Modelling
Tom J Stevenson
Queen Mary University of London
Stefan Wunsch
Karlsruhe Institute of Technology (KIT)
Omar A Zapata
Universidad de Antioquia
Journal of Physics: Conference Series
17426588 (ISSN) 17426596 (eISSN)
Vol. 898 7 072046
22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016
San Francisco, CA, USA,
San Francisco, CA, USA,
Subject Categories
Media and Communication Technology
Software Engineering
Computer Systems
DOI
10.1088/1742-6596/898/7/072046