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Laboratory for Machine Learning and Knowledge Representation

Development of machine learning algorithms and their applications in data mining and knowledge discovery tasks. 

Laboratory for Machine Learning and Knowledge Representation

 The Laboratory works on scientific research in the field of technical sciences and specifically on:

  • research and development of machine learning algorithms and their application in data analysis
  • theory and application of knowledge representation and reasoning algorithms
  • development of numerical algorithms for modeling of complex systems

Currently we work on:

  • development of algorithms for rule induction from large datasets
  • evaluation of outlier detection algorithms and development of a new approach based on random forests
  • data mining in social applications (political instability, direct foreign investment, international tourism industry)
  • detection of subgroups of patients by mining gene expression data
  • medical and technical ontologies as well as representation and reasoning for procedural knowledge
  • evaluation of the quality of computational annotations in the gene ontology
  • development and application of the GMDH based modeling
  • development of surrogate models of the reduced complexity for embedded computer systems

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