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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 1901–1925 of 2050 papers

TitleStatusHype
Bayesian Adaptive Matrix Factorization With Automatic Model Selection—0
Automatic Relevance Determination For Deep Generative Models—0
Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation—0
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
Adaptive Concentration of Regression Trees, with Application to Random ForestsCode0
Qualitative inequalities for squared partial correlations of a Gaussian random vector—0
Model selection of polynomial kernel regression—0
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
Dirichlet Process Parsimonious Mixtures for clustering—0
Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010—0
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics—0
On model misspecification and KL separation for Gaussian graphical models—0
Marginal likelihood and model selection for Gaussian latent tree and forest models—0
Model Selection in High-Dimensional Misspecified Models—0
Testing and Confidence Intervals for High Dimensional Proportional Hazards Model—0
The application of the Bayes Ying Yang harmony based GMMs in on-line signature verification—0
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