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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 1541–1550 of 2050 papers

TitleStatusHype
Cramer-Rao Bound for Estimation After Model Selection and its Application to Sparse Vector Estimation—0
Variational Bayes for high-dimensional linear regression with sparse priors—0
Deep Learning Inversion of Electrical Resistivity Data—0
Bayesian Neural Networks at Finite TemperatureCode0
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph ClusteringCode0
Adaptive Sequential Machine Learning—0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection—0
Combining Model and Parameter Uncertainty in Bayesian Neural NetworksCode0
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