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 1771–1780 of 2050 papers

TitleStatusHype
Identifying spatiotemporal dynamics of Ebola in Sierra Leone using virus genomes—0
Dynamic Model Selection for Prediction Under a Budget—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
On Bayesian Exponentially Embedded Family for Model Order Selection—0
Bandit-Based Model Selection for Deformable Object Manipulation—0
Cross-Validation with ConfidenceCode0
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms—0
High SNR Consistent Compressive Sensing—0
Exact Dimensionality Selection for Bayesian PCA—0
The variational Laplace approach to approximate Bayesian inference—0
Show:102550
← PrevPage 178 of 205Next →

No leaderboard results yet.