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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 1611–1620 of 2050 papers

TitleStatusHype
Cost-Sensitive Learning for Predictive Maintenance—0
A User-based Visual Analytics Workflow for Exploratory Model Analysis—0
Lexical Bias In Essay Level Prediction—0
Improving Subseasonal Forecasting in the Western U.S. with Machine LearningCode0
Comparison between Suitable Priors for Additive Bayesian Networks—0
Bayesian Structure Learning by Recursive Bootstrap—0
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
Simultaneous Localization and Layout Model Selection in Manhattan Worlds—0
Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion—0
View-graph Selection Framework for SfM—0
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