A novel multi-dimensional regression model based on Gaussian Networks

Llera Montero, Milton (2017). A novel multi-dimensional regression model based on Gaussian Networks. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: A novel multi-dimensional regression model based on Gaussian Networks
Author/s:
  • Llera Montero, Milton
Contributor/s:
  • Larrañaga Múgica, Pedro
  • Bielza Lozoya, Concepción
Item Type: Thesis (Master thesis)
Masters title: Inteligencia Artificial
Date: June 2017
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Modeling and prediction in continuous domains are one of the most important and studied problems in Mathematics and Computer Science. Models that can not only solve regression tasks, but also expose the interdependencies inside the domain are of high value for researchers in many fields. One of the most popular methods for learning the relations between variables in a continuous domain are Gaussian Networks. In this thesis we present a new model that can learn a Gaussian Network. This model can later be used for regression or analysis of the relations in the domain, with a particular interest in its application in the field of Neuroscience.---RESUMEN---Modelar y predecir en dominios continuos es uno de los problemas más estudiados en el campo de las Matemáticas y la Ciencia de la Computación. Modelos que no solamente puedan resolver problemas de regresión, si no también exponer las relaciones entre las variables de un dominio son de gran valor para los investigadores de muchos campos científicos. Uno de los modelos más populares usados para resolver estos problemas son las Redes Gaussianas. En esta tesis se presenta un nuevo modelo basado en Redes Gaussianas que puede ser ajustado a partir de datos para luego ser utilizado en tareas de regresión y análisis, con un especial interés en su aplicación al campo de la Neurociencia.

More information

Item ID: 48269
DC Identifier: http://oa.upm.es/48269/
OAI Identifier: oai:oa.upm.es:48269
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 26 Oct 2017 10:19
Last Modified: 26 Oct 2017 10:20
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