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Thijs Becker

Showing results (1-10 of 9) with videos related to

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American Journal of Orthodontics and Dentofacial Orthopedics : Official Publication of the American Association of Orthodontists, Its Constituent Societies, and the American Board of Orthodontics|November 26, 2023
Decision trees and random forestsThijs Becker, Axel-Jan Rousseau, Melvin Geubbelmans, et al.
American Journal of Orthodontics and Dentofacial Orthopedics : Official Publication of the American Association of Orthodontists, Its Constituent Societies, and the American Board of Orthodontics|December 28, 2023
BoostingThijs Becker, Melvin Geubbelmans, Axel-Jan Rousseau, et al.
Scientific Data|May 16, 2022
Motor evoked potentials for multiple sclerosis, a multiyear follow-up datasetJan Yperman, Veronica Popescu, Bart Van Wijmeersch, et al.
BMC Neurology|March 23, 2020
Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosisJan Yperman, Thijs Becker, Dirk Valkenborg, et al.
Sensors (Basel, Switzerland)|February 9, 2021
Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure DetectionThijs Becker, Kaat Vandecasteele, Christos Chatzichristos, et al.
Frontiers in Neuroinformatics|August 9, 2020
Deciphering the Morphology of Motor Evoked PotentialsJan Yperman, Thijs Becker, Dirk Valkenborg, et al.
Computer Methods and Programs in Biomedicine|November 8, 2021
Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180]Edward De Brouwer, Thijs Becker, Yves Moreau, et al.
Computer Methods and Programs in Biomedicine|June 19, 2021
Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progressionEdward De Brouwer, Thijs Becker, Yves Moreau, et al.
PLOS Digital Health|July 25, 2024
Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center studyEdward De Brouwer, Thijs Becker, Lorin Werthen-Brabants, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
American Journal of Orthodontics and Dentofacial Orthopedics : Official Publication of the American Association of Orthodontists, Its Constituent Societies, and the American Board of Orthodontics|November 26, 2023
Decision trees and random forestsThijs Becker, Axel-Jan Rousseau, Melvin Geubbelmans, et al.
American Journal of Orthodontics and Dentofacial Orthopedics : Official Publication of the American Association of Orthodontists, Its Constituent Societies, and the American Board of Orthodontics|December 28, 2023
BoostingThijs Becker, Melvin Geubbelmans, Axel-Jan Rousseau, et al.
Scientific Data|May 16, 2022
Motor evoked potentials for multiple sclerosis, a multiyear follow-up datasetJan Yperman, Veronica Popescu, Bart Van Wijmeersch, et al.
BMC Neurology|March 23, 2020
Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosisJan Yperman, Thijs Becker, Dirk Valkenborg, et al.
Sensors (Basel, Switzerland)|February 9, 2021
Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure DetectionThijs Becker, Kaat Vandecasteele, Christos Chatzichristos, et al.
Frontiers in Neuroinformatics|August 9, 2020
Deciphering the Morphology of Motor Evoked PotentialsJan Yperman, Thijs Becker, Dirk Valkenborg, et al.
Computer Methods and Programs in Biomedicine|November 8, 2021
Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180]Edward De Brouwer, Thijs Becker, Yves Moreau, et al.
Computer Methods and Programs in Biomedicine|June 19, 2021
Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progressionEdward De Brouwer, Thijs Becker, Yves Moreau, et al.
PLOS Digital Health|July 25, 2024
Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center studyEdward De Brouwer, Thijs Becker, Lorin Werthen-Brabants, et al.
Pageof 1