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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 211–220 of 2050 papers

TitleStatusHype
An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models—0
A Case for Dataset Specific Profiling—0
A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
A multi-stage machine learning model on diagnosis of esophageal manometry—0
Nonlinear Causal Discovery for Grouped Data—0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
A Multi-objective Exploratory Procedure for Regression Model Selection—0
Absolute convergence and error thresholds in non-active adaptive sampling—0
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