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Prior knowledge for learning networks in non-probabilistic settings

Author
Sangüesa, R.; Cortes, U.
Type of activity
Journal article
Journal
International journal of approximate reasoning
Date of publication
2000-04
Volume
24
Number
1
First page
103
Last page
120
DOI
https://doi.org/10.1016/S0888-613X(99)00046-8 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S0888613X99000468 Open in new window
Abstract
Current learning methods for general causal networks are basically data-driven. Exploration of the search space is made by resorting to some quality measure of prospective solutions. This measure is usually based on statistical assumptions. We discuss the interest of adopting a different point of view closer to machine learning techniques. Our main point is the convenience of using prior knowledge when it is available. We identify several sources of prior knowledge and define their role in the l...
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

Participants