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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

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Showing 401–410 of 2050 papers

TitleStatusHype
Automated Model Selection for Generalized Linear Models—0
Estimating the Number of Components in Finite Mixture Models via Variational Approximation—0
On uncertainty-penalized Bayesian information criterion—0
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
The CAST package for training and assessment of spatial prediction models in RCode2
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