Development of New Machine Learning Models Based on Gaussian Processes. Applications to Remote Sensing and Astrophysics
- Rafael Molina Soriano Zuzendaria
- Aggelos K. Katsaggelos Zuzendaria
Defentsa unibertsitatea: Universidad de Granada
Fecha de defensa: 2020(e)ko urria-(a)k 05
- Javier Mateos Delgado Presidentea
- Mari Luz García Martínez Idazkaria
- Sandra Morales Martínez Kidea
- Juan Gabriel Serra Pérez Kidea
- Valeriana Naranjo Ornedo Kidea
Mota: Tesia
Laburpena
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)