Search results for " Experimental results"

showing 2 items of 22 documents

Study of very forward energy and its correlation with particle production at midrapidity in pp and p-Pb collisions at the LHC

2022

Journal of high energy physics 08(8), 86 (2022). doi:10.1007/JHEP08(2022)086

perturbation theory [quantum chromodynamics]p p: scatteringNuclear and High Energy Physics:Kjerne- og elementærpartikkelfysikk: 431 [VDP]FOS: Physical scienceshiukkasfysiikkatransverse momentum[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]530114 Physical sciencesHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)ALICEHeavy Ion Experimentsscattering [p p]Heavy Ion Experiments ; calorimeter: forward spectrometer ; p: fragmentation ; quantum chromo ; dynamics: perturbation theory ; pp: scattering ; p nucleus: scattering ; parton: interaction ; CERN LHC Coll ; PYTHIA ; correlation ; Monte Carlo ; underlying event ; ALICE ; transverse momentum ; rapidity ; experimental results ; 13000 GeV-cms/nucleon ; 8160 GeV-cms/nucleon[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Nuclear Physics - Experimentddc:530p: fragmentationquantum chromodynamics: perturbation theoryNuclear Experiment (nucl-ex)parton: interactionNuclear ExperimentNuclear Experimentp nucleus: scatteringMonte Carlointeraction [parton]calorimeter: forward spectrometerunderlying eventscattering [p nucleus]8160 GeV-cms/nucleonfragmentation [p]forward spectrometer [calorimeter]:Nuclear and elementary particle physics: 431 [VDP]CERN LHC Collrapiditycorrelation13000 GeV-cms/nucleonPYTHIAParticle Physics - Experimentexperimental results
researchProduct

Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression

2020

Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods, the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM), in problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential, emphasizing the utility of the problem transformation especially with the EMLM. peerReviewed

the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM)koneoppiminenemphasizing the utility of the problem transformation especially with the EMLM.Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methodsin problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential
researchProduct