3 papers
cs.NE2026
Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression
Lukas Kammerer, Gabriel Kronberger, Deaglan J. Bartlett +3
We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions. We compare three we…
cs.LG2024
A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming
Yousef A. Radwan, Gabriel Kronberger, Stephan Winkler
Symbolic regression is a machine learning method with the goal to produce interpretable results. Unlike other machine learning methods such as, e.g. random forests or neural networ…
cs.CV2024
Nuclear Pleomorphism in Canine Cutaneous Mast Cell Tumors: Comparison of Reproducibility and Prognostic Relevance between Estimates, Manual Morphometry and Algorithmic Morphometry
Andreas Haghofer, Eda Parlak, Alexander Bartel +18
Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurem…