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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 931–940 of 2050 papers

TitleStatusHype
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
Factor-Augmented Regularized Model for Hazard Regression—0
Feature-based model selection for object detection from point cloud data—0
A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: application to porcine aortic valves—0
Partial sequence labeling with structured Gaussian Processes—0
De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs—0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
Towards Deep Learning-aided Wireless Channel Estimation and Channel State Information Feedback for 6G—0
Model Selection in High-Dimensional Block-Sparse Linear Regression—0
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
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