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 1231–1240 of 2050 papers

TitleStatusHype
Structure Learning in Gaussian Graphical Models from Glauber Dynamics—0
Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control—0
Student-t Processes as Alternatives to Gaussian Processes—0
Subjectivity in Unsupervised Machine Learning Model Selection—0
Subsampling Graphs with GNN Performance Guarantees—0
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data—0
Superpixel-guided Two-view Deterministic Geometric Model Fitting—0
Supervised structure learning—0
Straight-Through meets Sparse Recovery: the Support Exploration Algorithm—0
Surfing the modeling of PoS taggers in low-resource scenarios—0
Show:102550
← PrevPage 124 of 205Next →

No leaderboard results yet.