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 1131–1140 of 2050 papers

TitleStatusHype
mikropml: User-Friendly R Package for Supervised Machine Learning PipelinesCode1
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD GeneralizationCode1
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming—0
Unsupervised Offline Changepoint Detection EnsemblesCode1
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection—0
Model Selection CNN-based VVC QualityEnhancement—0
Order flow in the financial markets from the perspective of the Fractional Lévy stable motion—0
Speech Decomposition Based on a Hybrid Speech Model and Optimal Segmentation—0
Synthetic Data for Model Selection—0
SMLSOM: The shrinking maximum likelihood self-organizing mapCode0
Show:102550
← PrevPage 114 of 205Next →

No leaderboard results yet.