• Incremental methods for Bayesian network learning 

      Roure Alcobé, Josep; Sangüesa i Sole, Ramon (1999-10)
      Report de recerca
      Accés obert
      Current methods for learning Bayesian Networks are mainly batch methods. That is, they are supposed to act in a single step over the complete set of data. We remark the need to develop new approaches that do not require ...
    • Learning causal networks from data 

      Sangüesa i Sole, Ramon (1996-03)
      Report de recerca
      Accés obert
      Causal concepts play a crucial role in many reasoning tasks. Organized as a model revealing the causal structure of a domain, they can guide inference through relevant knowledge. This is a specially difficult knowledge ...
    • NetExpert : sistema basado en inteligencia artificial para la localización de expertos 

      Pujol Serra, Josep M.; Sangüesa i Sole, Ramon (Escola Tècnica Superior d'Enginyers de Telecomunicació de Barcelona, 2001)
      Article
      Accés obert
    • Porqpine: a peer-to-peer search engine 

      Pujol, Josep Maria; Sangüesa i Sole, Ramon; Bermúdez, Juanjo (2003-05)
      Report de recerca
      Accés obert
      In this paper, we present a fully distributed and collaborative search engine for web pages: Porqpine. This system uses a novel query-based model and collaborative filtering techniques in order to obtain user-customize ...
    • Probabilistic conditional independence: a similarity-based measure and its application to causal network learning 

      Sangüesa i Sole, Ramon; Cabós, Joan; Cortés García, Claudio Ulises (1996-06)
      Report de recerca
      Accés obert
      A new definition for similarity between possibility distributions is introduced and discussed as a basis for detecting dependence between variables by measuring the similarity degree of their respective distributions. This ...
    • The ACE recommender system 

      Sangüesa i Sole, Ramon; Vázquez Huerga, Alberto; Vázquez Salceda, Javier (2001-04)
      Report de recerca
      Accés obert
      In this report we present the ACE Recommender System, a system built using the Multi Agent technology. In a practical way we study the use of cognitive and collaborative filtering to improve the accuracity of the ...