Abstract: We propose a soft gradient boosting framework for sequential regression that embeds a learnable linear feature transform within the boosting procedure. At each boosting iteration, we train a ...
APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering ...
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ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...
ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...
Purpose: This study aimed to develop three types of machine learning (ML) models based on gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) to explore ...
Container fruit trees are a lovely way for those who have a smaller garden to grow their own lemons, figs, and more. However, over time, these trees will eventually outgrow their home and need to be ...
Abstract: We propose an online tracking algorithm that adaptively models target appearances based on an online gradient boosting decision tree. Our algorithm is particularly useful for non-rigid ...
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