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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 1591–1600 of 2050 papers

TitleStatusHype
Recurrent Neural Networks for Fuzz Testing Web BrowsersCode0
Physics-Based Learning for Robotic Environmental Sensing—0
Bitcoin Forecasting Using ARIMA and PROPHET—0
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process—0
Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection—0
Learning Vine Copula Models For Synthetic Data Generation—0
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors—0
Variational Selection of Features for Molecular Kinetics—0
LM-BIC Model Selection in Semiparametric Models—0
Model Evaluation, Model Selection, and Algorithm Selection in Machine LearningCode0
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