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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 1711–1720 of 2050 papers

TitleStatusHype
Network Model Selection Using Task-Focused Minimum Description Length—0
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data—0
Adaptive multi-penalty regularization based on a generalized Lasso pathCode0
Nonsparse learning with latent variables—0
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics—0
DAGGER: A sequential algorithm for FDR control on DAGsCode0
The Merging Path Plot: adaptive fusing of k-groups with likelihood-based model selectionCode0
Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning—0
Modeling Sheep pox Disease from the 1994-1998 Epidemic in Evros Prefecture, Greece—0
MSM lag time cannot be used for variational model selection—0
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