=Paper=
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|id=Vol-1433/deraedt
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1433/deraedt.pdf
|volume=Vol-1433
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1433/deraedt.pdf</pdf>
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Technical Communications of ICLP 2015. Copyright with the Authors.                        1




Using and Developing Declarative Languages for
      Machine Learning and Data Mining
                                      Luc De Raedt
         Department of Computer Science, Katholieke Universiteit Leuven, Belgium
                         (e-mail: luc.deraedt@cs.kuleuven.be)




                                        Abstract
Following a general trend in artificial intelligence, the fields machine learning and data
mining have recently witnessed a growing interest in the use of declarative techniques.
What is essential in this paradigm is that the user be provided with a way to declaratively
specify what the problem is rather than having to outline how that solution needs to be
computed. This corresponds to a model + solver-based approach in which the user specifies
the problem in a high level modelling language and the system automatically transforms
such models into a format that can be used by a solver to efficiently generate a solution.
This should be much easier for the user than having to implement or adapt an algorithm
that computes a particular solution to a specific problem. Therefore, declarative methods
could have a radical impact on the fields of machine learning and data mining.
   In this talk, I shall report on this new trend in machine learning and data mining
from two different perspectives. The first is that of a user of existing declarative methods
such as constraint programming and answer set programming, where I shall report on
experiences, successes and challenges with using this type of technology. The second is
that of a developer of declarative languages and solvers for machine learning and data
mining, where I shall provide a gentle introduction to different types of languages such
as inductive query languages, which extend database query languages with primitives for
mining and learning, modelling languages for constraint-based mining, and probabilistic
and other programming languages that support machine learning.

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