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Publications

  1. Paolo Avesani; Paolo Massa; Michele Nori; Angelo Susi,
    Collaborative Radio Community,
    Proceedings of the 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems,
    London,
    Springer Verlag,
    2002
    , (2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems,
    Malaga, Spain,
    May 29-31, 2002)
  2. Angelo Susi; Anna Perini; Emanuele Olivetti,
    iEMSs 2002 Integrated Assessment and Decision Support,
    2002
    , pp. 426-
    431
    , (iEMSs 2002 Integrated Assessment and Decision Support,
    Lugano, Switzerland,
    24/06/2002 - 27/06/2002)
  3. Conor Michael Hayes; Paolo Massa; Paolo Avesani; P. Cunningham,
    An on-line Evaluation Framework for Recommender Systems,
    Proceedings of the AH2002 Workshop on Recommendation and Personalization in e-Commerce,
    2002
  4. Paolo Avesani,
    Evaluation Framework for Local Ontologies Interoperability,
    Proceedings of the AAAI-02 Workshop on Meaning Negotiation [MeaN-02],
    2002
  5. S. Aguzzoli; Paolo Avesani; Paolo Massa,
    Collaborative Case-Based Recommender Systems,
    Proceedings of the 6th European Conference on Case-Based Reasoning [ECCBR 2002],
    Springer,
    vol.2416,
    2002
    , pp. 460-
    474
    , (Advances in Case-Based Reasoning, 6th European Conference, ECCBR 2002,
    Aberdeen, Scotland, UK,
    September 4-7, 2002)
  6. C. Hayes;P. Massa;P. Avesani;P. Cunningham,
    Workshop on Personalization and Recommendation in E-Commerce (Malaga,
    2002
    , (Workshop on Personalization and Recommendation in E-Commerce,
    Malaga, Spain,
    28/05/2002)
  7. P. Avesani;P. Massa,
    Recommendation and Personalization From Item to Collection.,
    ECCBR Workshops'02,
    2002
    , pp. 8-
    9
    , (ECCBR Workshops'02,
    Aberdeen, Scotland, UK,
    September 4-7, 2002)
  8. Paolo Avesani; Emanuele Olivetti; Angelo Susi,
    Feeding Data Mining,
    Data mining is a complex process that aims to derive an accurate predictive model starting from a collection of data. Traditional approaches assume that data are given in advance and their quality, size and structure are independent parameters. In this paper we argue that and axtended vision of data mining should include the step of data acquisition as part of the overall process. Moreover the static view should be replaced by an evolving perspective that conceives the data mining as an iterative process where data acquisition and data analysis repeatedly follow each other. A decision support tool based on data mining will have to be extended accordingly. Decision making will be concerned not only with a predictive purpose but also with a policy for a next data acquisition step. A successful data acquisition strategy will have to take into account both future model accuracy and the cost associate to the acquisition of each feature. To find a trade off between these two components is an open issue. A fremework to focus this new challenging problem is proposed,
    2002
  9. Luca Faes; Giandomenico Nollo; Michele Kirchner; Emanuele Olivetti; F. Gaita; R. Riccardi; Renzo Antolini,
    Principal component analysis and cluster analysis for measuring the local organization of human atrial fibrillation,
    in «MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING»,
    vol. 39,
    2001
    , pp. 656 -
    663
  10. S. Aguzzoli; Paolo Avesani; Paolo Massa,
    Compositional CBR via Collaborative Filtering,
    Proceedings of ICCBR Workshop on Case-Based Reasoning in E-Commerce,
    2001

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