Abstract
Recently, some community focuses on computer aided engineering inductive applications, as seen in workshops, such as AAAI98/ICML98 workshop on ‘The Methodology of Applying Machine Learning’ and ECML98 workshop on ‘Upgrading Learning to the Meta-level: Model Selection and Data Transformation‘. It is time to decompose inductive learning algorithms and organize inductive learning methods (ILMs) for reconstructing inductive learning systems. Given such ILMs, we may invent a new inductive learning system that works well to a given data set by re-interconnecting ILMs
Similar content being viewed by others
References
Gertjan van Heijst, “The Role of Ontologies in Knowledge Engineering”, Dr Thesis, University of Amsterdam, 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Suyama, A., Negishi, N., Yamagchi, T. (1998). Composing Inductive Applications Using Ontologies for Machine Learning. In: Arikawa, S., Motoda, H. (eds) Discovey Science. DS 1998. Lecture Notes in Computer Science(), vol 1532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49292-5_55
Download citation
DOI: https://doi.org/10.1007/3-540-49292-5_55
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65390-5
Online ISBN: 978-3-540-49292-4
eBook Packages: Springer Book Archive