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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 1151–1160 of 2050 papers

TitleStatusHype
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Model Selection for Time Series Forecasting: Empirical Analysis of Different EstimatorsCode0
Model Selection's Disparate Impact in Real-World Deep Learning Applications—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
OTCE: A Transferability Metric for Cross-Domain Cross-Task RepresentationsCode1
A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity—0
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation—0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
The Shape of Learning Curves: a ReviewCode0
Modeling the Second Player in Distributionally Robust OptimizationCode1
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