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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 1101–1110 of 2050 papers

TitleStatusHype
Variational Inference and Model Selection with Generalized Evidence Bounds—0
Variational Selection of Features for Molecular Kinetics—0
Vector autoregression models with skewness and heavy tails—0
Verifying Learning-Based Robotic Navigation Systems—0
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions—0
View-graph Selection Framework for SfM—0
Virtual Reference Feedback Tuning with data-driven reference model selection—0
A User-based Visual Analytics Workflow for Exploratory Model Analysis—0
Visual Sentiment Analysis from Disaster Images in Social Media—0
Volatility Forecasting with 1-dimensional CNNs via transfer learning—0
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