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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 221–230 of 2050 papers

TitleStatusHype
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
A model selection approach for clustering a multinomial sequence with non-negative factorization—0
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process—0
Bayesian Adaptive Matrix Factorization With Automatic Model Selection—0
Bayesian CART models for insurance claims frequency—0
A ModelOps-based Framework for Intelligent Medical Knowledge Extraction—0
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems—0
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning—0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
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