Machine Learning Developments in ROOT
Paper i 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

Författare

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, Rymd- och geovetenskap, Global miljömätteknik

Tom J Stevenson

Queen Mary University of London

Stefan Wunsch

Karlsruher Institut für Technologie (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,

Ämneskategorier

Medieteknik

Programvaruteknik

Datorsystem

DOI

10.1088/1742-6596/898/7/072046

Mer information

Senast uppdaterat

2022-10-14