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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 1801–1810 of 2050 papers

TitleStatusHype
Robust and Parallel Bayesian Model Selection—0
Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion—0
Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression—0
Detection of intensity bursts using Hawkes processes: an application to high frequency financial data—0
Communication-efficient Distributed Sparse Linear Discriminant Analysis—0
Nonparametric Bayesian inference of the microcanonical stochastic block model—0
Model Selection for Gaussian Process Regression by Approximation Set Coding—0
Searching parsimonious solutions with GA-PARSIMONY and XGboost in high-dimensional databases—0
Universum Learning for Multiclass SVM—0
Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background—0
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