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 1281–1290 of 2050 papers

TitleStatusHype
The variational Laplace approach to approximate Bayesian inference—0
Thompson Sampling-like Algorithms for Stochastic Rising Bandits—0
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity—0
Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation—0
Time Resolution Dependence of Information Measures for Spiking Neurons: Atoms, Scaling, and Universality—0
Time Series Anomaly Detection with label-free Model Selection—0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
Topic Modeling and Link-Prediction for Material Property Discovery—0
Topic Stability over Noisy Sources—0
Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey—0
Show:102550
← PrevPage 129 of 205Next →

No leaderboard results yet.