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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 1421–1430 of 2050 papers

TitleStatusHype
Multiclass Learning from ContradictionsCode0
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural CircuitsCode0
On model selection for scalable time series forecasting in transport networks—0
Generalised Linear Models for Dependent Binary Outcomes with Applications to Household Stratified Pandemic Influenza DataCode0
Improving Model Robustness Using Causal Knowledge—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection—0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
A multiple testing framework for diagnostic accuracy studies with co-primary endpointsCode0
Selective machine learning of doubly robust functionals—0
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