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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 1791–1800 of 2050 papers

TitleStatusHype
Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models—0
Dependence model assessment and selection with DecoupleNets—0
Deploying Offline Reinforcement Learning with Human Feedback—0
Derivative-Free Reinforcement Learning: A Review—0
Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case—0
Detecting seasonal episodic-like spatiotemporal memory patterns using animal movement modelling—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
Design and Scheduling of an AI-based Queueing System—0
Designing Ecosystems of Intelligence from First Principles—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
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