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 1921–1930 of 2050 papers

TitleStatusHype
Testing and Confidence Intervals for High Dimensional Proportional Hazards Model—0
The application of the Bayes Ying Yang harmony based GMMs in on-line signature verification—0
Deep Domain Confusion: Maximizing for Domain InvarianceCode1
Quantile universal threshold: model selection at the detection edge for high-dimensional linear regression—0
How Many Communities Are There?—0
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models—0
Convex Techniques for Model Selection—0
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation—0
Clustering evolving data using kernel-based methods—0
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets—0
Show:102550
← PrevPage 193 of 205Next →

No leaderboard results yet.