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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 1441–1450 of 2050 papers

TitleStatusHype
Action-State Dependent Dynamic Model Selection—0
Active Comparison of Prediction Models—0
Active Learning Algorithms for Graphical Model Selection—0
Active Learning for Undirected Graphical Model Selection—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning—0
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
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