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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 1431–1440 of 2050 papers

TitleStatusHype
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image SegmentationCode1
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
Global Adaptive Generative Adjustment—0
Enhancing Certifiable Robustness via a Deep Model Ensemble—0
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems—0
Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control—0
Model selection for deep audio source separation via clustering analysis—0
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
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