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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 101–110 of 2050 papers

TitleStatusHype
Nearest Neighbour Equilibrium ClusteringCode0
Advancements in Natural Language Processing: Exploring Transformer-Based Architectures for Text Understanding—0
Face Spoofing Detection using Deep LearningCode0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Patch-based learning of adaptive Total Variation parameter maps for blind image denoising—0
Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks—0
Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal PredictionCode0
Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing DataCode0
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly DetectionCode0
End-to-End Edge AI Service Provisioning Framework in 6G ORAN—0
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