Title: Towards Programming Languages for Machine Learning and Data Mining (Extended Abstract)
Authors: De Raedt, Luc ×
Nijssen, Siegfried #
Issue Date: 2011
Publisher: Springer
Host Document: Lecture Notes in Computer Science vol:6804 pages:25-32
Conference: Foundations of Intelligent Systems - International Symposium, ISMIS 2011 edition:19 location:Warsaw, Poland date:June 28-30, 2011
Abstract: Today there is only little support for developing software
that incorporates a machine learning or a data mining component. To
alleviate this situation, we propose to develop programming languages
for machine learning and data mining. We also argue that such languages
should be declarative and should be based on constraint programming
modeling principles. In this way, one could declaratively specify the problem
of machine learning or data mining problem of interest in a high-level
modeling language and then translate it into a constraint satisfaction
or optimization problem, which could then be solved using particular
solvers. These ideas are illustrated on problems of constraint-based itemset
and pattern set mining.
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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