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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 1781–1790 of 2050 papers

TitleStatusHype
Deep Learning For Smile Recognition—0
Deep Learning Inversion of Electrical Resistivity Data—0
Deep Learning Models for UAV-Assisted Bridge Inspection: A YOLO Benchmark Analysis—0
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022—0
On model selection for scalable time series forecasting in transport networks—0
Deep Online Convex Optimization by Putting Forecaster to Sleep—0
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation—0
Deep Time Series Models for Scarce Data—0
Degrees of Freedom and Information Criteria for the Synthetic Control Method—0
Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models—0
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