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 951–960 of 2050 papers

TitleStatusHype
Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity—0
Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—0
Inferring bias and uncertainty in camera calibration—0
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees—0
A Bayesian Approach to Network Modularity—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
Inferring Network Structure From Data—0
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter—0
Show:102550
← PrevPage 96 of 205Next →

No leaderboard results yet.