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Exploiting inductive bias shift in knowledge acquisition from ill-structured domains

Author
Talavera, L.; Cortes, U.
Type of activity
Journal article
Journal
Lecture notes in computer science
Date of publication
1997-10
Volume
1319
First page
377
Last page
382
DOI
https://doi.org/10.1007/BFb0026804 Open in new window
URL
http://link.springer.com/chapter/10.1007/BFb0026804 Open in new window
Abstract
Machine Learning (ML) methods are very powerful tools to automate the knowledge acquisition (KA) task. Particularly, in illstructured domains where there is no clear idea about which concepts exist, inductive unsupervised learning systems appear to be a promising approach to help experts in the early stages of the acquisition process. In this paper we examine the concept of inductive bias, which have received great attention from the ML community, and discuss the importance of bias shift when us...
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

Participants