Memorias de investigación
Artículos en revistas:
Linear Latent Force Models using Gaussian Processes
Año:2013
Áreas de investigación
  • Inferencia de procesos estocásticos,
  • Ecuaciones diferenciales,
  • Inteligencia artificial (redes neuronales, lógica borrosa, sistemas expertos, etc),
  • Reconocimiento de patrones
Datos
Descripción
Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.
Internacional
Si
JCR del ISI
Si
Título de la revista
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
Factor de impacto JCR
4,795
Información de impacto
Volumen
35
DOI
10.1109/TPAMI.2013.86
Número de revista
11
Desde la página
2693
Hasta la página
2705
Mes
NOVIEMBRE
Ranking
ENG., ELECTRICAL & ELECTRONIC [Q1: 5/242] COMPUTER SCI., ARTIFICIAL INT [Q1: 4/114]
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Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Ingeniería de Circuitos y Sistemas