Development of New Machine Learning Models Based on Gaussian Processes. Applications to Remote Sensing and Astrophysics

• Autores: Pablo Morales Álvarez
• Directores de la Tesis: Rafael Molina Soriano (dir. tes.) , Aggelos K. Katsaggelos (dir. tes.)
• Lectura: En la Universidad de Granada ( España ) en 2020
• Idioma: inglés
• ISBN: 9788413066660
• Número de páginas: 186
• Tribunal Calificador de la Tesis: Javier Mateos Delgado (presid.) , Mari Luz García Martínez (secret.) , Juan Gabriel Serra Pérez (voc.) , Valeriana Naranjo Ornedo (voc.) , Sandra Morales Martínez (voc.)
• Enlaces
  • Tesis en acceso abierto en: DIGIBUG
• Resumen

  • 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)