Conference proceeding
An Approach to Re-representation in Relational Learning
ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE OF THE CATALAN ASSOCIATION FOR ARTIFICIAL INTELLIGENCE, v 256
01 Jan 2013
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We present a new approach lo learn from relational data based on re-representation of the examples. This approach, called property-based re-representation is based on a new analysis of the structure of refinement graphs used in ILP and relational learning in general. This analysis allows the characterization of relational examples by a set of multi-relational patterns called properties. Using them, we perform a property-based re-representation of relational examples that facilitates the development of relational learning techniques.
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Details
- Title
- An Approach to Re-representation in Relational Learning
- Creators
- Santiago Ontanon - Drexel Univ, Dept Comp Sci, Philadelphia, PA 19104 USAEnric Plaza - CSIC, IIIA, Spanish Council Sci Res, Artif Intelligence Res Inst, Bellaterra E-08193, Spain
- Contributors
- K Gibert (Editor)Botti (Editor)R ReigBolano (Editor)
- Publication Details
- ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE OF THE CATALAN ASSOCIATION FOR ARTIFICIAL INTELLIGENCE, v 256
- Series
- Frontiers in Artificial Intelligence and Applications
- Publisher
- Ios Press
- Number of pages
- 10
- Grant note
- TIN2009-13692-C03-01 / Next-CBR 2009-SGR-1434 / Generalitat de Catalunya; General Electric TIN2012-38450- C03-03 / Cognitio
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000342668500003
- Scopus ID
- 2-s2.0-84894735002
- Other Identifier
- 991019167906304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods