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 1561–1570 of 2050 papers

TitleStatusHype
A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example—0
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval—0
A Symmetry-based Framework for Model Selection of Coral Reef Population Growth Models—0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables—0
Asymptotic Model Selection for Directed Networks with Hidden Variables—0
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Bayesian Model Selection—0
Show:102550
← PrevPage 157 of 205Next →

No leaderboard results yet.