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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 1901–1910 of 2050 papers

TitleStatusHype
Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study—0
Cats & Co: Categorical Time Series Coclustering—0
Kernel Spectral Clustering and applications—0
Response-based Learning for Machine Translation of Open-domain Database Queries—0
Model Selection and Overfitting in Genetic Programming: Empirical Study [Extended Version]—0
Meta learning of bounds on the Bayes classifier error—0
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood—0
Time Resolution Dependence of Information Measures for Spiking Neurons: Atoms, Scaling, and Universality—0
Comparison of Bayesian predictive methods for model selection—0
Indian Buffet process for model selection in convolved multiple-output Gaussian processesCode0
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