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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 91–100 of 2050 papers

TitleStatusHype
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
BarcodeBERT: Transformers for Biodiversity AnalysisCode1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
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