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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 1621–1630 of 2050 papers

TitleStatusHype
Bayesian Nonparametrics: An Alternative to Deep Learning—0
Bayesian optimization for automated model selection—0
Bayesian Optimization for Selecting Efficient Machine Learning Models—0
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach—0
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns—0
Bayesian Robust Tensor Factorization for Incomplete Multiway Data—0
Bayesian stochastic blockmodeling—0
Bayesian Structure Learning by Recursive Bootstrap—0
Bayesian taut splines for estimating the number of modes—0
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