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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 1061–1070 of 2050 papers

TitleStatusHype
Qualitative inequalities for squared partial correlations of a Gaussian random vector—0
Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice—0
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces—0
Quantifying the Capability Boundary of DeepSeek Models: An Application-Driven Performance Analysis—0
Quantifying Uncertainty and Variability in Machine Learning: Confidence Intervals for Quantiles in Performance Metric Distributions—0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles—0
Quantile Factor Models—0
Quantile universal threshold: model selection at the detection edge for high-dimensional linear regression—0
Quantized Neural Networks: Characterization and Holistic Optimization—0
Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities—0
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