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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 1211–1220 of 2050 papers

TitleStatusHype
Sparsity-Agnostic Linear Bandits with Adaptive Adversaries—0
Bayesian Spatial Predictive Synthesis—0
Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010—0
Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network—0
Speech Decomposition Based on a Hybrid Speech Model and Optimal Segmentation—0
Speedy Model Selection (SMS) for Copula Models—0
Spike and slab variational Bayes for high dimensional logistic regression—0
Spiking Neural Networks Hardware Implementations and Challenges: a Survey—0
Split LBI: An Iterative Regularization Path with Structural Sparsity—0
Stabilizing black-box model selection with the inflated argmax—0
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