Joseph Knight

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—Given a raster spatial framework, as well as training and test sets, the spatial decision tree learning (SDTL) problem aims to minimize classification errors as well as salt-and-pepper noise. The SDTL problem is important due to many societal applications such as land cover classification in remote sensing. However, the SDTL problem is challenging due to(More)
Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as(More)
UNLABELLED In sub-Saharan Africa, cryptococcal meningitis (CM) continues to be a predominant cause of AIDS-related mortality. Understanding virulence and improving clinical treatments remain important. To characterize the role of the fungal strain genotype in clinical disease, we analyzed 140 Cryptococcus isolates from 111 Ugandans with AIDS and CM.(More)
Given learning samples from a spatial raster dataset, the geographical classification problem aims to learn a decision tree classifier that minimizes classification errors as well as salt-n-pepper noise. The problem is important in many applications, such as land cover classification in remote sensing and lesion classification in medical diagnosis. However,(More)
Broadband mobile networks utilize a radio resource control (RRC) state machine to allocate scarce radio resources. Current implementations introduce high latencies and cross-layer degradation. Recently, the RRC enhancements, continuous packet connectivity (CPC) and the enhanced forward access channel (Enhanced FACH), have emerged in UMTS. We study the(More)
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