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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 1921–1930 of 2050 papers

TitleStatusHype
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsCode0
Offline detection of change-points in the mean for stationary graph signalsCode0
Ranking vs. Classifying: Measuring Knowledge Base Completion QualityCode0
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and FairnessCode0
Know2Vec: A Black-Box Proxy for Neural Network RetrievalCode0
Solar Flare Forecast: A Comparative Analysis of Machine Learning Algorithms for Solar Flare Class PredictionCode0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
LaF: Labeling-Free Model Selection for Automated Deep Neural Network ReusingCode0
Solution Path Algorithm for Twin Multi-class Support Vector MachineCode0
Best Arm Identification for Stochastic Rising BanditsCode0
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