Tagging heavy flavours with boosted decision trees
数据分析、统计与概率
2007-05-23 v2 高能物理 - 实验
摘要
This paper evaluates the performance of boosted decision trees for tagging b-jets. It is shown, using a Monte Carlo simulation of events that boosted decision trees outperform feed-forward neural networks. The results show that for a b-tagging efficiency of 60% the light jet rejection given by boosted decision trees is about 35% higher than that given by neural networks.
引用
@article{arxiv.physics/0702041,
title = {Tagging heavy flavours with boosted decision trees},
author = {J. Bastos},
journal= {arXiv preprint arXiv:physics/0702041},
year = {2007}
}
备注
12 pages, 13 figures