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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 791–800 of 2050 papers

TitleStatusHype
Classification with Scattering Operators—0
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions—0
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models—0
Action-State Dependent Dynamic Model Selection—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model—0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
A simple application of FIC to model selection—0
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