SOTAVerified

Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1451–1460 of 2050 papers

TitleStatusHype
Adaptive debiased machine learning using data-driven model selection techniques—0
Adaptive Design Optimization in Experiments with People—0
Adaptive LASSO estimation for functional hidden dynamic geostatistical model—0
Adaptive Model Selection Framework: An Application to Airline Pricing—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Adaptive Online Learning—0
Adaptive Sequential Machine Learning—0
Adaptive variational Bayes: Optimality, computation and applications—0
A data-centric approach to class-specific bias in image data augmentation—0
A deep learning based solution for construction equipment detection: from development to deployment—0
Show:102550
← PrevPage 146 of 205Next →

No leaderboard results yet.