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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 271–280 of 2050 papers

TitleStatusHype
On discretely structured growth models and their moments—0
Peeling Back the Layers: An In-Depth Evaluation of Encoder Architectures in Neural News RecommendersCode2
Thermodynamic Bayesian Inference—0
Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series ClassificationCode0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Factors in Fashion: Factor Analysis towards the Mode—0
MCUBench: A Benchmark of Tiny Object Detectors on MCUs—0
Efficient Bias Mitigation Without Privileged Information—0
Source-Free Domain Adaptation for YOLO Object DetectionCode2
Scalable Ensemble Diversification for OOD Generalization and DetectionCode0
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