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 1641–1650 of 2050 papers

TitleStatusHype
Probabilistic Boolean Tensor DecompositionCode0
Variational Inference and Model Selection with Generalized Evidence Bounds—0
Using J-K fold Cross Validation to Reduce Variance When Tuning NLP ModelsCode0
Learning Equations for Extrapolation and ControlCode0
Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical ModelCode0
Structured Variational Learning of Bayesian Neural Networks with Horseshoe PriorsCode0
Stationary Geometric Graphical Model Selection—0
Degrees of Freedom and Model Selection for k-means ClusteringCode0
Agreement-based Learning—0
Structural Learning of Multivariate Regression Chain Graphs via Decomposition—0
Show:102550
← PrevPage 165 of 205Next →

No leaderboard results yet.