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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 1321–1330 of 2050 papers

TitleStatusHype
Is it worth it? Budget-related evaluation metrics for model selection—0
Is K-fold cross validation the best model selection method for Machine Learning?—0
IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation—0
Joint Continuous and Discrete Model Selection via Submodularity—0
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion—0
Kauffman's adjacent possible in word order evolution—0
Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models—0
Kernel-based Information Criterion—0
Kernel Spectral Clustering and applications—0
KITE: A Kernel-based Improved Transferability Estimation Method—0
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