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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 1931–1940 of 2050 papers

TitleStatusHype
Greedy metrics in orthogonal greedy learning—0
Sparse Modeling for Image and Vision Processing—0
Bayesian Evidence and Model Selection—0
Median Selection Subset Aggregation for Parallel Inference—0
Model Selection for Topic Models via Spectral Decomposition—0
Bayesian Robust Tensor Factorization for Incomplete Multiway Data—0
Graphical LASSO Based Model Selection for Time Series—0
Learning manifold to regularize nonnegative matrix factorization—0
Unsupervised learning of regression mixture models with unknown number of components—0
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection—0
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