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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 1471–1480 of 2050 papers

TitleStatusHype
Agreement-based Learning—0
Unsupervised Model Selection for Variational Disentangled Representation Learning—0
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models—0
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization—0
A Junction Tree Framework for Undirected Graphical Model Selection—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
A Latent Gaussian Mixture Model for Clustering Longitudinal Data—0
eGAD! double descent is explained by Generalized Aliasing Decomposition—0
A linearized framework and a new benchmark for model selection for fine-tuning—0
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