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 1071–1080 of 2050 papers

TitleStatusHype
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction—0
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning—0
UniTN End-to-End Discourse Parser for CoNLL 2016 Shared Task—0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
Universal Approximation of Edge Density in Large Graphs—0
Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks—0
Universum Learning for Multiclass SVM—0
Un modèle Bayésien de co-clustering de données mixtes—0
Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery—0
Unreflected Use of Tabular Data Repositories Can Undermine Research Quality—0
Show:102550
← PrevPage 108 of 205Next →

No leaderboard results yet.