Results 71 to 80 of about 6,391,262 (321)

Symbolic Interval-Valued Regression Model

Journal of Statistical Theory and Applications (JSTA)
Symbolic data analysis can provide statistical inferences for macroscale data while preserving as much information as possible from microscale data. In this study, we focus on the symbolic interval-valued regression model.
Liang-Ching Lin
doaj   +1 more source

Selecting Informative Data Samples for Model Learning Through Symbolic Regression

IEEE Access, 2021
Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy
Erik Derner, Jiri Kubalik, Robert Babuska   +2 more
doaj   +1 more source

Symbolic regression extracted models.

, 2019
Symbolic regression extracted models.
Vsevolod Peysakhovich (6470357), Almoctar Hassoumi (6470354), Christophe Hurter (6470360)   +2 more
core   +1 more source

Upper Cervical Cord Area as a Biomarker of Conversion to Secondary Progressive Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective This study assessed whether upper cervical cord area (UCCA) measured on routine brain MRI can serve as a biomarker of conversion to SPMS. Methods This is a single‐center retrospective cohort study of RRMS patients with cross‐sectional and longitudinal analyses of clinical and MRI data. Future SPMS converters were matched by age, sex,
Nabil K. El Ayoubi, Sola Al Ghazal, Salem Hannoun, Medhat A. Siddik, Nour M. Najjar, Jim Dagher, Joseph Abboud, Myriam G. Semaan, Samia J. Khoury   +8 more
wiley   +1 more source

SymFormer: End-to-End Symbolic Regression Using Transformer-Based Architecture

IEEE Access
Many real-world systems can be naturally described by mathematical formulas. The task of automatically constructing formulas to fit observed data is called symbolic regression.
Martin Vastl, Jonas Kulhanek, Jiri Kubalik, Erik Derner, Robert Babuska   +4 more
doaj   +1 more source

Taylor genetic programming for symbolic regression

Proceedings of the Genetic and Evolutionary Computation Conference, 2022
Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR) problems. Compared with the machine learning or deep learning methods that depend on the pre-defined model and the training dataset for solving SR problems, GP is more focused on finding the solution in a search space.
Baihe He, Qiang Lu 0005, Qingyun Yang, Jake Luo, Zhiguang Wang   +4 more
openaire   +4 more sources

Evaluation of Dried Plasma Spot‐Based Quantification of Glial Fibrillary Acidic Protein as a Disease‐Associated Biomarker in Neuromyelitis Optica Spectrum Disorder

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To evaluate the diagnostic accuracy of glial fibrillary acidic protein (GFAP) measured in dried plasma spots versus conventional plasma‐ and serum‐GFAP testing for assessment of disease severity in aquaporin‐4 immunoglobulin G–positive neuromyelitis optica spectrum disorder (AQP4‐IgG+ NMOSD).
Felix Wohlrab, Evelyn Alvarez, Roua Hamdi, Bingqing Zhang, Hayeun Ji, Niyati Jhaveri, Patrick Schindler, Pedro Sanchez, Pia S. Sperber, Tanja Schmitz‐Hübsch, Frederike C. Oertel, Nisa Vorasoot, Najib Kissani, Falko Böhringer, Jens Kuhle, Anne‐Katrin Pröbstel, Xiao‐Jun Ma, Péter Körtvelyessy, Patrick Waters, Friedemann Paul   +19 more
wiley   +1 more source

Universal Approach to Solution of Optimization Problems by Symbolic Regression

Applied Sciences, 2021
Optimization problems and their solution by symbolic regression methods are considered. The search is performed on non-Euclidean space. In such spaces it is impossible to determine a distance between two potential solutions and, therefore, algorithms ...
Elena Sofronova, Askhat Diveev
doaj   +1 more source

Matching Large Biomedical Ontologies Using Symbolic Regression Using Symbolic Regression

Journal of Data Intelligence, 2022
The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Ontology matching methods are of great importance today since they allow us to find the pivot points from which an automatic data integration process can be established ...
Jorge Martinez-Gil, Shaoyi Yin, Josef Küng, Franck Morvan   +3 more
openaire   +2 more sources

White Matter and Perivascular Imaging Changes in Alzheimer's Disease and Cerebral Amyloid Angiopathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peak‐width of skeletonized mean diffusivity (PSMD) and diffusion tensor imaging–analysis along the perivascular space (DTI‐ALPS), reflecting white matter integrity and glymphatic function, are altered in Alzheimer's disease (AD).
Debina Laishram, Guangwei Du, Sangam Kanekar, Nandakumar Nagaraja   +3 more
wiley   +1 more source