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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 1361–1370 of 2050 papers

TitleStatusHype
Learning Structural Kernels for Natural Language Processing—0
Learning the hypotheses space from data through a U-curve algorithm—0
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
Learning the Hypotheses Space from data Part II: Convergence and Feasibility—0
Learning the Markov order of paths in a network—0
Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
Learning under Singularity: An Information Criterion improving WBIC and sBIC—0
Learning Vine Copula Models For Synthetic Data Generation—0
Learning with many experts: model selection and sparsity—0
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