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 1731–1740 of 2050 papers

TitleStatusHype
Nonparametric weighted stochastic block models—0
Sparse model selection via integral terms—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption—0
LIMSI@CoNLL'17: UD Shared Task—0
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering—0
UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise FeaturesCode0
Probabilistic models of individual and collective animal behavior—0
Familia: An Open-Source Toolkit for Industrial Topic ModelingCode0
Consistent Nonparametric Different-Feature Selection via the Sparsest k-Subgraph Problem—0
Show:102550
← PrevPage 174 of 205Next →

No leaderboard results yet.