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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 241–250 of 2050 papers

TitleStatusHype
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Bayesian Model Selection Methods for Mutual and Symmetric k-Nearest Neighbor Classification—0
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling—0
Automated Model Selection for Generalized Linear Models—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Automated Model Selection for Time-Series Anomaly Detection—0
Automated Model Selection with Bayesian Quadrature—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
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