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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 1191–1200 of 2050 papers

TitleStatusHype
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models—0
A Characterization for Optimal Bundling of Products with Non-Additive Values—0
Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach—0
Supervised Momentum Contrastive Learning for Few-Shot Classification—0
How do some Bayesian Network machine learned graphs compare to causal knowledge?—0
Online and Scalable Model Selection with Multi-Armed Bandits—0
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization—0
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection—0
Cost-based feature selection for network model choice—0
Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability—0
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