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 571–580 of 2050 papers

TitleStatusHype
Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory—0
A Sentiment Analysis of Medical Text Based on Deep Learning—0
A Realistic Protocol for Evaluation of Weakly Supervised Object LocalizationCode0
On the Necessity of Collaboration for Online Model Selection with Decentralized Data—0
Semantic Approach to Quantifying the Consistency of Diffusion Model Image GenerationCode0
Measuring Domain Shifts using Deep Learning Remote Photoplethysmography Model Similarity—0
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces—0
Model Selection with Model Zoo via Graph LearningCode0
Predictive Analytics of Varieties of PotatoesCode0
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems—0
Show:102550
← PrevPage 58 of 205Next →

No leaderboard results yet.