Development of New Machine Learning Models Based on Gaussian Processes. Applications to Remote Sensing and Astrophysics Morales Álvarez, Pablo Molina Soriano, Rafael Katsaggelos, Aggelos K. Universidad de Granada. Programa de Doctorado en Física y Matemáticas Gaussian Processes Machine learning Remote sensing Astrophysics In this PhD thesis we have developed different machine learning models based on Gaussian Processes. Different settings (regression, classification and crowdsourcing) are considered, and various application fields (specially remote sensing and astrophysics, but also threat detection and sentiment analysis) are targeted. The main global conclusion of this PhD thesis is the versatility of Gaussian Processes to model different scenarios (regression, classification, crowdsourcing) and target various applications (remote sensing, security, astrophysics), either as the central algorithm to perform the task at hand (Chapters 2-7) or as an auxiliary tool to be integrated within a larger model (Chapter 8) 2020-10-30T08:53:09Z 2020-10-30T08:53:09Z 2020 2020-10-05 info:eu-repo/semantics/doctoralThesis Morales Álvarez, Pablo. Development of New Machine Learning Models Based on Gaussian Processes. Applications to Remote Sensing and Astrophysics. Granada: Universidad de Granada, 2020. [http://hdl.handle.net/10481/63966] 9788413066660 http://hdl.handle.net/10481/63966 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España Universidad de Granada