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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 741–750 of 2050 papers

TitleStatusHype
Comparing Bayesian Models of Annotation—0
A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example—0
A linearized framework and a new benchmark for model selection for fine-tuning—0
Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases—0
A Survey on Theoretical Advances of Community Detection in Networks—0
eGAD! double descent is explained by Generalized Aliasing Decomposition—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Comparative Analysis of Predicting Subsequent Steps in Hénon Map—0
Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission—0
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
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