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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 1551–1560 of 2050 papers

TitleStatusHype
A Statistical Framework for Model Selection in LSTM Networks—0
A Statistical-Modelling Approach to Feedforward Neural Network Model Selection—0
A Statistical Theory of Deep Learning via Proximal Splitting—0
A Strong Baseline for Batch Imitation Learning—0
On the Existence of Simpler Machine Learning Models—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
A study on the distribution of social biases in self-supervised learning visual models—0
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance—0
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
A Survey on Theoretical Advances of Community Detection in Networks—0
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