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 1651–1660 of 2050 papers

TitleStatusHype
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
Black-box Selective Inference via Bootstrapping—0
Block-diagonal covariance selection for high-dimensional Gaussian graphical models—0
Blocked Clusterwise Regression—0
Blockout: Dynamic Model Selection for Hierarchical Deep Networks—0
Boosted Zero-Shot Learning with Semantic Correlation Regularization—0
Boosting for Efficient Model Selection for Syntactic Parsing—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Bootstrap based asymptotic refinements for high-dimensional nonlinear models—0
Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation—0
Show:102550
← PrevPage 166 of 205Next →

No leaderboard results yet.