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 861–870 of 2050 papers

TitleStatusHype
Greedy metrics in orthogonal greedy learning—0
Greedy Model Averaging—0
Bayesian Boosting for Linear Mixed Models—0
Green Runner: A tool for efficient deep learning component selection—0
Green Runner: A tool for efficient model selection from model repositories—0
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—0
High SNR Consistent Compressive Sensing—0
Group-Sparse Model Selection: Hardness and Relaxations—0
Guided Recommendation for Model Fine-Tuning—0
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model—0
Show:102550
← PrevPage 87 of 205Next →

No leaderboard results yet.