Title: Languages for learning and mining
Authors: De Raedt, Luc
Issue Date: Jan-2015
Publisher: AAAI Press
Host Document: Proceedings 29th AAAI Conference on Artificial Intelligence
Conference: AAAI edition:29 location:Austin, Texas, US date:25-30 January 2015
Abstract: Applying machine learning and data mining to novel applications is cumbersome. This observation is the prime motivation for the interest in languages for learning and mining. This note provides a gentle introduction to three types of languages that support machine learn- ing and data mining: inductive query languages, which extend database query languages with primitives for mining and learning, modelling languages, which allow to declaratively specify and solve mining and learning problems, and programming languages, that support the learning of functions and subroutines. It uses an example of each type of language to introduce the underlying ideas and puts them into a common perspective. This then forms the basis for a short analysis of the state-of- the-art.
Description: Senior Member Track Paper
Publication status: accepted
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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