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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 1541–1550 of 2050 papers

TitleStatusHype
Nonparametric Hierarchical Clustering of Functional Data—0
Nonparametric Independence Screening via Favored Smoothing Bandwidth—0
Nonparametric Variable Screening with Optimal Decision Stumps—0
Nonparametric weighted stochastic block models—0
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data—0
Nonsparse learning with latent variables—0
No Regularization is Needed: An Efficient and Effective Model for Incomplete Label Distribution Learning—0
Not again! Data Leakage in Digital Pathology—0
Fast Approximation and Estimation Bounds of Kernel Quadrature for Infinitely Wide Models—0
Objective Bayesian Analysis for Change Point Problems—0
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