Results 21 to 30 of about 65,604 (247)
Active Learning in Symbolic Regression with Physical Constraints
CoRR, 2023 Evolutionary symbolic regression (SR) fits a symbolic equation to data, which gives a concise interpretable model. We explore using SR as a method to propose which data to gather in an active learning setting with physical constraints. SR with active learning proposes which experiments to do next.
Jorge Medina, Andrew D. White
openaire +2 more sources
Physically regularized machine learning emulators of aerosol activation [PDF]
Geoscientific Model Development, 2020 Abstract. The activation of aerosol into cloud droplets is an important step in the formation of clouds and strongly influences the radiative budget of the Earth. Explicitly simulating aerosol activation in Earth system models is challenging due to the computational complexity required to resolve the necessary chemical and physical processes and their ...
Sam J. Silva +3 more
openaire +3 more sources
Failure-Averse Active Learning for Physics-Constrained Systems
IEEE Transactions on Automation Science and Engineering, 2023 Active learning is a subfield of machine learning that is devised for design and modeling of systems with highly expensive sampling costs. Industrial and engineering systems are generally subject to physics constraints that may induce fatal failures when they are violated, while such constraints are frequently underestimated in active learning. In this
Cheolhei Lee +3 more
openaire +5 more sources
International Journal of Behavioral Nutrition and Physical Activity, 2020 Background UK and global policies recommend whole-school approaches to improve childrens’ inadequate physical activity (PA) levels. Yet, recent meta-analyses establish current interventions as ineffective due to suboptimal implementation rates and poor ...
Andy Daly-Smith +17 more
doaj +1 more source
Revista Internacional de Ciencias del Deporte, 2020 The present study analyzes a strategy implemented in a public school in order to increase scholars´ physical activity levels during theoretical subjects.
Beatriz Polo-Recuero +2 more
doaj +1 more source
Archives of Rehabilitation Research and Clinical Translation, 2023 Objective: To explore physical activity trajectories during the discharge transition phase after in-hospital rehabilitation after acquired brain injury (ABI). Design: A cross-sectional observational study.
Helene Honoré, MSc +4 more
doaj +1 more source
Metabolites, 2019 Associations between multicollinear accelerometry-derived physical activity (PA) data and cardiometabolic health in children needs to be analyzed using an approach that can handle collinearity among the explanatory variables.
Eivind Aadland +3 more
doaj +1 more source
Active learning for the optimal design of multinomial classification in physics
Physical Review Research, 2022 Optimal design for model training is a critical topic in machine learning. Active Learning aims at obtaining improved models by querying samples with maximum uncertainty according to the estimation model for artificially labeling; this has the additional advantage of achieving successful performances with a reduced number of labeled samples. We analyze
Yongcheng Ding +4 more
openaire +4 more sources
South African Journal of Education, 2022 The acquisition of vocabulary and narrative comprehension are key abilities for children’s literacy development and to potentiate cognitive and academic skills from early ages.
Alba Cámara-Martínez +3 more
doaj +1 more source
International Journal of Behavioral Nutrition and Physical Activity, 2019 Background The analysis of associations between accelerometer-derived physical activity (PA) intensities and cardiometabolic health is a major challenge due to multicollinearity between the explanatory variables.
Eivind Aadland +4 more
doaj +1 more source