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Decision tree and its generalization of random forests are a simple yet powerful machine learning model for many classification and regression problems. Recent works propose how to privately evaluate ...
We study the variability of predictions made by bagged learners and random forests, and show how to estimate standard errors for these methods. Our work builds on variance estimates for bagging propos...
n atmospheric models, due to their computational time or resource limitations, physical processes have to be simulated using reduced (i.e. simplified) models.
It is widely accepted that land degradation and desertification (LDD) are serious global threats to humans and the environment. Around a third of savannahs in Africa are affected by LDD processes that...
The increasing use of electrical energy has yielded more necessities of electric utilities including transmission lines and electric pylons which require a real-time risk monitoring to prevent massive...
The development of lidar system, especially incorporated with high-resolution camera components, has shown great potential for urban classification. However, how to automatically select the best featu...
Various multi-echo and Full-waveform (FW) lidar features can be processed. In this paper, multiple classifers are applied to lidar feature selection for urban scene classification. Random fores...

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