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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 1821–1830 of 2050 papers

TitleStatusHype
Incremental Learning for Fully Unsupervised Word Segmentation Using Penalized Likelihood and Model Selection—0
Lower Bounds on Active Learning for Graphical Model Selection—0
Ordering as privileged information—0
Making Tree Ensembles Interpretable: A Bayesian Model Selection ApproachCode0
A review of Gaussian Markov models for conditional independence—0
Interpretability in Linear Brain Decoding—0
Model-Agnostic Interpretability of Machine Learning—0
Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP)—0
Simultaneous Clustering and Model Selection for Tensor Affinities—0
Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso—0
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