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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 1851–1860 of 2050 papers

TitleStatusHype
Efficient Bias Mitigation Without Privileged Information—0
Efficient Cross-Validation for Semi-Supervised Learning—0
Efficient Deep Reinforcement Learning Requires Regulating Overfitting—0
Efficient Distributed Estimation of Inverse Covariance Matrices—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
Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning—0
Efficient Model Selection for Time Series Forecasting via LLMs—0
Efficient model selection in switching linear dynamic systems by graph clustering—0
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