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 2021–2030 of 2050 papers

TitleStatusHype
Scikit-learn: Machine Learning in PythonCode0
Large Scale Correlation Clustering OptimizationCode0
Sparse Estimation with Structured Dictionaries—0
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers—0
Greedy Model Averaging—0
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC—0
On U-processes and clustering performance—0
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions—0
Face Recognition using Optimal Representation Ensemble—0
The LASSO risk: asymptotic results and real world examples—0
Show:102550
← PrevPage 203 of 205Next →

No leaderboard results yet.