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Full-Text Articles in Physical Sciences and Mathematics
Measuring Machine Learning Model Uncertainty With Applications To Aerial Segmentation, Kevin James Cotton
Measuring Machine Learning Model Uncertainty With Applications To Aerial Segmentation, Kevin James Cotton
CGU Theses & Dissertations
Machine learning model performance on both validation data and new data can be better measured and understood by leveraging uncertainty metrics at the time of prediction. These metrics can improve the model training process by indicating which training data need to be corrected and what part of the domain needs further annotation. The methods described have yet to reach mainstream adoption, and show great potential. Here, we survey the field of uncertainty metrics and provide a robust framework for its application to aerial segmentation. Uncertainty is divided into two types: aleatoric and epistemic. Aleatoric uncertainty arises from variations in training …
A Tacticians Guide To Conflict, Vol. 1: Advancing Explanations & Predictions Of Intrastate Conflict, Khaled Eid
A Tacticians Guide To Conflict, Vol. 1: Advancing Explanations & Predictions Of Intrastate Conflict, Khaled Eid
CGU Theses & Dissertations
Intrastate conflict is an ever-evolving problem – causes, explanation, and predictions are increasingly murky as traditional methods of analysis focus on structural issues as precursors of conflict. Often times these theories do not consider the underlying meso and micro dynamics that can provide vital insights into the phenomena. Tactical decision-makers are left using models that rely on highly aggregated, country level data to create proper courses of actions (COAs) to address or predict conflict. The shortcoming is that conflicts morph quite rapidly and structural variables can struggle capture such dynamic changes. To address this some tacticians are using big data …