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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 1831–1840 of 2050 papers

TitleStatusHype
Model-Agnostic Interpretability of Machine Learning—0
Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP)—0
Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso—0
Simultaneous Clustering and Model Selection for Tensor Affinities—0
Predictive Coarse-Graining—0
Bayesian Model Selection of Stochastic Block Models—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Bayesian Variable Selection for Globally Sparse Probabilistic PCA—0
The Quality of the Covariance Selection Through Detection Problem and AUC Bounds—0
Combinatorially Generated Piecewise Activation Functions—0
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