Brush is a new symbolic regression method that integrates tree-like rules with function optimization, matching or beating decision trees and forests on clinical scoring tasks while producing simpler interpretable models.
Parameter identification for symbolic regression using nonlinear least squares , volume =
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Post-selection with DL or FBF after multi-objective GP search improves test-set performance over AIC/BIC baselines on noisy synthetic and real regression tasks, while using DL directly as fitness often causes premature convergence to overly simple models.
Symbolic emulators approximate key Lambda CDM functions to 0.001-0.05% accuracy across relevant redshifts and Omega_m values, enabling faster 3x2pt inference with consistent results.
citing papers explorer
-
Towards symbolic regression for interpretable clinical decision scores
Brush is a new symbolic regression method that integrates tree-like rules with function optimization, matching or beating decision trees and forests on clinical scoring tasks while producing simpler interpretable models.
-
Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions
Post-selection with DL or FBF after multi-objective GP search improves test-set performance over AIC/BIC baselines on noisy synthetic and real regression tasks, while using DL directly as fitness often causes premature convergence to overly simple models.
-
Symbolic Emulators for Cosmology: Accelerating Cosmological Analyses Without Sacrificing Precision
Symbolic emulators approximate key Lambda CDM functions to 0.001-0.05% accuracy across relevant redshifts and Omega_m values, enabling faster 3x2pt inference with consistent results.