Publications associées à Decision stump

Improved calorimetric particle identification in NA62 using machine learning techniques

Alessandro Mapelli, Radoslav Marchevski, Alina Kleimenova

Measurement of the ultra-rare K+ -> pi(+)nu(nu) over bar over bar decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decis ...

Springer Nature2023

Prospects for B-c(+) -> tau(+)nu(tau) at FCC-ee

Donal Patrick Hill, Clement Helsens, Marie Hartmann

This paper presents the prospects for a precise measurement of the branching fraction of the leptonic B-c(+) -> tau(+)nu(tau) decay at the Future Circular Collider (FCC-ee) running at the Z-pole. A detailed description of the simulation and analysis framew ...

Springer Nature2021

Dirac vs. Majorana HNLs (and their oscillations) at SHiP

Inar Timiryasov

SHiP is a proposed high-intensity beam dump experiment set to operate at the CERN SPS. It is expected to have an unprecedented sensitivity to a variety of models containing feebly interacting particles, such as Heavy Neutral Leptons (HNLs). Two HNLs or mor ...

Springer Nature2020

Adaptive Sampling for Large Scale Boosting

François Fleuret, Charles Dubout

Classical Boosting algorithms, such as AdaBoost, build a strong classifier without concern for the computational cost. Some applications, in particular in computer vision, may involve millions of training examples and very large feature spaces. In such con ...

2014

Learning decision rules for energy efficient building control

Colin Neil Jones

While rule based control (RBC) is current practice in most building automation systems that issue discrete control signals, recent simulation studies suggest that advanced, optimization based control methods such as hybrid model predictive control (HMPC) c ...

Elsevier2014

Boosting Binary Keypoint Descriptors

Pascal Fua, Vincent Lepetit, Tomasz Piotr Trzcinski, Christos Marios Christoudias

Binary keypoint descriptors provide an efficient alternative to their floating-point competitors as they enable faster processing while requiring less memory. In this paper, we propose a novel framework to learn an extremely compact binary descriptor we ca ...

2013

Learning Near-optimal Decision Rules for Energy Efficient Building Control

Colin Neil Jones

Recent studies suggest that advanced optimization based control methods such as model predictive control (MPC) can increase energy efficiency of buildings. However, adoption of these methods by industry is still slow, as building operators are used to work ...

2012

Learning Near-optimal Decision Rules for Energy Efficient Building Control

Colin Neil Jones

Recent studies suggest that advanced optimization based control methods such as model predictive control (MPC) can increase energy efficiency of buildings. However, adoption of these methods by industry is still slow, as building operators are used to work ...

2012

Multivariate Boosting with Look-Up Tables for Face Processing

Cosmin Atanasoaei

This thesis proposes a novel unified boosting framework. We apply this framework to the several face processing tasks, face detection, facial feature localisation, and pose classification, and use the same boosting algorithm and the same pool of features ( ...

EPFL2012

Measurement of the atmospheric neutrino energy spectrum from 100 GeV to 400 TeV with IceCube

Shirit Cohen, Mathieu Ribordy, Matthias Wolf, Xiaoxia Bai

A measurement of the atmospheric muon neutrino energy spectrum from 100 GeV to 400 TeV was performed using a data sample of about 18 000 up-going atmospheric muon neutrino events in IceCube. Boosted decision trees were used for event selection to reject mi ...

American Physical Society2011