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

Autor
Talavera, L.; Cortes, U.
Tipus d'activitat
Article en revista
Revista
Lecture notes in computer science
Data de publicació
1997-10
Volum
1319
Pàgina inicial
377
Pàgina final
382
DOI
https://doi.org/10.1007/BFb0026804 Obrir en finestra nova
URL
http://link.springer.com/chapter/10.1007/BFb0026804 Obrir en finestra nova
Resum
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...
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic