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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 911–920 of 2050 papers

TitleStatusHype
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Differentially Private Generalized Linear Models Revisited—0
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained ModelsCode0
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net—0
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual TransferCode0
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts—0
Differentially Private Learning with Margin Guarantees—0
Improved Group Robustness via Classifier Retraining on Independent SplitsCode0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
Trinary Tools for Continuously Valued Binary Classifiers—0
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