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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 1771–1780 of 2050 papers

TitleStatusHype
Bandit-Based Model Selection for Deformable Object Manipulation—0
Cross-Validation with ConfidenceCode0
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms—0
High SNR Consistent Compressive Sensing—0
Exact Dimensionality Selection for Bayesian PCA—0
The variational Laplace approach to approximate Bayesian inference—0
Modeling non-stationarities in high-frequency financial time series—0
Objective Bayesian Analysis for Change Point Problems—0
Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry DataCode0
metboost: Exploratory regression analysis with hierarchically clustered dataCode0
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