On the Automation of Similarity Information Maintenance in Flexible Query Answering Systems

  • Balázs Csanád Csáji
  • Josef Küng
  • Jürgen Palkoska
  • Roland Wagner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3180)


This paper proposes a method for automatic maintaining the similarity information for a particular class of Flexible Query Answering Systems (FQAS). The paper describes the three main levels of this approach: the first one deals with learning the distance measure through interaction with the user. Machine-learning techniques, such as reinforcement learning, can be used to achieve this. The second level tries to build a good representation of the learned distance measure. This level uses distance geometry and multidimensional optimization methods. The last level of automation uses statistical optimization techniques to further decrease the dimension of the similarity data.


Independent Component Analysis Independent Component Analysis Minimal Dimension Distance Geometry Euclidean Distance Matrix 
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 2004

Authors and Affiliations

  • Balázs Csanád Csáji
    • 1
  • Josef Küng
    • 2
  • Jürgen Palkoska
    • 2
  • Roland Wagner
    • 2
  1. 1.Computer and Automation Research InstituteHungarian Academy of SciencesBudapestHungary
  2. 2.Institute for Applied Knowledge Processing (FAW)Johannes Kepler UniversityLinzAustria

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