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 401–410 of 2050 papers

TitleStatusHype
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI—0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
A simple application of FIC to model selection—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
Classification with Scattering Operators—0
Classification with Sparse Overlapping Groups—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
Show:102550
← PrevPage 41 of 205Next →

No leaderboard results yet.