Learning Profiles Based on Hierarchical Hidden Markov Model

  • Ugo Galassi
  • Attilio Giordana
  • Lorenza Saitta
  • Maco Botta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3488)


This paper presents a method for automatically constructing a sophisticated user/process profile from traces of user/process behavior. User profile is encoded by means of a Hierarchical Hidden Markov Model (HHMM). The HHMM is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. The method described here is based on a recent algorithm, which is able to synthesize the HHMM structure from a set of logs of the user activity. The algorithm follows a bottom-up strategy, in which elementary facts in the sequences (motives) are progressively grouped, thus building the abstraction hierarchy of a HHMM, layer after layer. The method is firstly evaluated on artificial data. Then a user identification task, from real traces, is considered. A preliminary experimentation with several different users produced encouraging results.


Speech Recognition Viterbi Algorithm Average Error Rate Real Trace Abstraction Hierarchy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ugo Galassi
    • 1
  • Attilio Giordana
    • 1
  • Lorenza Saitta
    • 1
  • Maco Botta
    • 2
  1. 1.Dipartimento di InformaticaUniversità Amedeo AvogadroAlessandriaItaly
  2. 2.Dipartimento di InformaticaUniversitá di TorinoTorinoItaly

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