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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 561–570 of 2050 papers

TitleStatusHype
Face Spoofing Detection using Deep LearningCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsCode0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Deep Generalized Method of Moments for Instrumental Variable AnalysisCode0
Deeper Insights into Graph Convolutional Networks for Semi-Supervised LearningCode0
fETSmcs: Feature-based ETS model component selectionCode0
Effects of sampling skewness of the importance-weighted risk estimator on model selectionCode0
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