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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 1221–1230 of 2050 papers

TitleStatusHype
Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases—0
General Bayesian time-varying parameter VARs for predicting government bond yields—0
Partially Hidden Markov Chain Linear Autoregressive model: inference and forecastingCode0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Two Sides of Meta-Learning Evaluation: In vs. Out of DistributionCode0
Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies—0
Bridging factor and sparse models—0
Joint Continuous and Discrete Model Selection via Submodularity—0
Geostatistical Learning: Challenges and OpportunitiesCode0
Parsimonious Modelling for Estimating Hospital Cooling Demand to Improve Energy Efficiency—0
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