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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 2031–2040 of 2050 papers

TitleStatusHype
Discriminative Clustering by Regularized Information Maximization—0
PAC-Bayesian Model Selection for Reinforcement Learning—0
Probabilistic latent variable models for distinguishing between cause and effect—0
Classification with Scattering Operators—0
Regularization for Cox's proportional hazards model with NP-dimensionality—0
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical ModelsCode0
Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation—0
Sparsistent Learning of Varying-coefficient Models with Structural Changes—0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
Adaptive Design Optimization in Experiments with People—0
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