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Articles 61 - 62 of 62
Full-Text Articles in Data Science
Change Point Detection For Streaming Data Using Support Vector Methods, Charles Harrison
Change Point Detection For Streaming Data Using Support Vector Methods, Charles Harrison
Electronic Theses and Dissertations, 2020-2023
Sequential multiple change point detection concerns the identification of multiple points in time where the systematic behavior of a statistical process changes. A special case of this problem, called online anomaly detection, occurs when the goal is to detect the first change and then signal an alert to an analyst for further investigation. This dissertation concerns the use of methods based on kernel functions and support vectors to detect changes. A variety of support vector-based methods are considered, but the primary focus concerns Least Squares Support Vector Data Description (LS-SVDD). LS-SVDD constructs a hypersphere in a kernel space to bound …
Bayesian Spatiotemporal Modeling With Gaussian Processes, Qing He
Bayesian Spatiotemporal Modeling With Gaussian Processes, Qing He
Electronic Theses and Dissertations, 2020-2023
Bayesian spatiotemporal models have been successfully applied to various fields of science, such as ecology and epidemiology. The complicated nature of spatiotemporal patterns can be well represented through priors such as Gaussian processes. This dissertation is focused on two applications of Bayesian spatiotemporal models: a) anomaly detection for spatiotemporal data with missingness and b) zero-inflated spatiotemporal count data analysis. Missingness in spatiotemporal data prohibits anomaly detection algorithms from learning characteristic rules and patterns due to the lack of most data. This project is motivated by a challenge provided by the National Science Foundation (NSF) and the National Geospatial-Intelligence Agency (NGA). …