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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
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
The topology of large Open Connectome networks for the human brain—0
Fast rates with high probability in exp-concave statistical learning—0
Sampling Requirements for Stable Autoregressive Estimation—0
Efficient Distributed Estimation of Inverse Covariance Matrices—0
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