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Symbolic Regression
ISBN/GTIN

Beschreibung

Symbolic regression (SR) is one of the most powerful machine learning techniques that produces transparent models, searching the space of mathematical expressions for a model that represents the relationship between the predictors and the dependent variable without the need of taking assumptions about the model structure.
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Details

Weitere ISBN/GTIN9780429679537
ProduktartE-Book
EinbandE-Book
FormatPDF
Erscheinungsdatum16.08.2024
Auflage24001 A. 1. Auflage
Seiten308 Seiten
SpracheEnglisch
Dateigrösse13222 Kbytes
Illustrationen36 schwarz-weiße und 100 farbige Abbildungen, 36 schwarz-weiße und 100 farbige Zeichnungen, 33 schwarz-weiße Tabellen
Artikel-Nr.11938067
KatalogVC
Datenquelle-Nr.5201406
Weitere Details

Autor

The authors are all affiliated with the University of Applied Sciences (UAS) Upper Austria.

Gabriel Kronberger is professor for data engineering and business intelligence. His research interests are symbolic regression and machine learning as well as probabilistic graphical models.

Bogdan Burlacu is a research assistant. His main focus is the study of genetic programming evolutionary dynamics in symbolic regression scenarios.

Michael Kommenda is a research assistant. He has been applying symbolic regression methods in various industrial projects and application scenarios.

Stephan M. Winkler is professor for medical and bioinformatics and head of the bioinformatics research group. His research interests despite bioinformatics include genetic programming, nonlinear model identification and machine learning.

Michael Affenzeller is professor for heuristic optimization and machine learning and head of the Heuristic and Evolutionary Algorithms Laboratory. Furthermore, he is the vice dean for research and overall head of the COMET project for heuristic optimization in production and logistics (HOPL).