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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 481–490 of 2050 papers

TitleStatusHype
Music Genre Classification: A Comparative Analysis of CNN and XGBoost Approaches with Mel-frequency cepstral coefficients and Mel Spectrograms—0
Understanding Short-Term Implied Volatility Dynamics: A Model-Independent Approach Beyond Stochastic Volatility—0
Comprehensive Exploration of Synthetic Data Generation: A Survey—0
U-Trustworthy Models.Reliability, Competence, and Confidence in Decision-Making—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
Explainable Adaptive Tree-based Model Selection for Time Series Forecasting—0
Enhancing the Power of OOD Detection via Sample-Aware Model Selection—0
LEAD: Exploring Logit Space Evolution for Model Selection—0
Automated Model Selection for Tabular DataCode0
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models—0
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