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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 661–670 of 2050 papers

TitleStatusHype
Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings—0
Automated Model Selection for Time-Series Anomaly Detection—0
A multi-stage machine learning model on diagnosis of esophageal manometry—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
A Case for Dataset Specific Profiling—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
CRIX an index for cryptocurrencies—0
Cramer-Rao Bound for Estimation After Model Selection and its Application to Sparse Vector Estimation—0
Automated Model Selection for Generalized Linear Models—0
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