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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 1591–1600 of 2050 papers

TitleStatusHype
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting—0
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI—0
AutoML-GPT: Large Language Model for AutoML—0
A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting—0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
A variational synthesis of evolutionary and developmental dynamics—0
A Vision for Semantically Enriched Data Science—0
Awkt: A Physicochemical Parameter Estimation Tool for Capillary Zone Electrophoresis—0
A Work Zone Simulation Model for Travel Time Prediction in a Connected Vehicle Environment—0
Bandit-Based Model Selection for Deformable Object Manipulation—0
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