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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 1241–1250 of 2050 papers

TitleStatusHype
A linearized framework and a new benchmark for model selection for fine-tuning—0
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models—0
Choice modelling in the age of machine learning - discussion paper—0
Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach—0
A Characterization for Optimal Bundling of Products with Non-Additive Values—0
Supervised Momentum Contrastive Learning for Few-Shot Classification—0
Online and Scalable Model Selection with Multi-Armed Bandits—0
How do some Bayesian Network machine learned graphs compare to causal knowledge?—0
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization—0
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection—0
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