SOTAVerified

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 1521–1530 of 2050 papers

TitleStatusHype
Near-Field Localization with Physics-Compliant Electromagnetic Model: Algorithms and Model Mismatch Analysis—0
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits—0
Network cross-validation by edge sampling—0
Network Estimation by Mixing: Adaptivity and More—0
Network Model Selection for Task-Focused Attributed Network Inference—0
Network Model Selection Using Task-Focused Minimum Description Length—0
Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis—0
Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models—0
Neural Active Learning with Performance Guarantees—0
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics—0
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