Signal Processing and Speech Communication Laboratory
hometheses & projects › Reduced Precision Bayesian Network Classifiers

Reduced Precision Bayesian Network Classifiers

Master Thesis
Announcement date
02 Oct 2012
Research Areas

Short Description

Bayesian Network Classifiers (BNCs) are probabilistic classifiers that can be applied in a wide range of applications, e.g. digit recognition, medical expert systems or speech recognition. While these classifiers are theoretically well understood, there are little results on real-world implementations available. Aim of this project is to close this gap. This includes:

  • Theory:
    • Literature review
    • Learning of BNCs that are suited for implementation in hardware, i.e. definition and implementation of appropriate learning algorithms
    • Evaluation of the proposed learning algorithms
    • If possible, derivation of suboptimality bounds on these BNCs in comparison to “optimal” BNCs
  • Practice:
    • Implementation of BNCs on FPGAs and comparison of different implementation options
    • Evaluation of the performance of the implemented system
    • Usage of BNCs in a real world application, e.g. handwritten digit recognition

Illustration of a BNC with naive Bayes structure

Your Profile/Requirements

The candidate should be interested in machine learning, applied mathematics/statistics, Matlab programming, and algorithms. Interested candidates are encouraged to ask for further information. Additionally, the supervision of own projects in one of the above mention fields is possible.


Franz Pernkopf ( or 0316/873 4436)


Sebastian Tschiatschek, Peter Reinprecht, Manfred Mücke, Franz Pernkopf: Bayesian Network Classifiers with Reduced Precision Parameters. ECML/PKDD (1) 2012: 74-89;