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

  1. Morales Álvarez, Pablo
Zuzendaria:
  1. Rafael Molina Soriano Zuzendaria
  2. Aggelos K. Katsaggelos Zuzendaria

Defentsa unibertsitatea: Universidad de Granada

Fecha de defensa: 2020(e)ko urria-(a)k 05

Epaimahaia:
  1. Javier Mateos Delgado Presidentea
  2. Mari Luz García Martínez Idazkaria
  3. Sandra Morales Martínez Kidea
  4. Juan Gabriel Serra Pérez Kidea
  5. Valeriana Naranjo Ornedo Kidea
Saila:
  1. CIENCIAS DE LA COMPUTACIÓN E INTELIGENCIA ARTIFICIAL

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)