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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 641–650 of 2050 papers

TitleStatusHype
Efficient Cross-Validation for Semi-Supervised Learning—0
Efficient Deep Reinforcement Learning Requires Regulating Overfitting—0
Efficient Distributed Estimation of Inverse Covariance Matrices—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
Adaptive Sequential Machine Learning—0
Efficient Estimation of the number of neighbours in Probabilistic K Nearest Neighbour Classification—0
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming—0
Efficient Model Compression for Bayesian Neural Networks—0
Achieving Fairness with a Simple Ridge Penalty—0
Entropy-based Characterization of Modeling Constraints—0
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