Gradient Learning Algorithms for Ontology Computing
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-10-29
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
The gradient learning model has been raising great attention in view of its promising perspectives for applications in statistics, data dimensionality reducing, and other specific fields.
In this paper, we raise a new gradient learning model for ontology similarity measuring and ontology mapping in multidividing setting.
The sample error in this setting is given by virtue of the hypothesis space and the trick of ontology dividing operator.
Finally, two experiments presented on plant and humanoid robotics field verify the efficiency of the new computation model for ontology similarity measure and ontology mapping applications in multidividing setting.
American Psychological Association (APA)
Gao, Wei& Zhu, Linli. 2014. Gradient Learning Algorithms for Ontology Computing. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1034648
Modern Language Association (MLA)
Gao, Wei& Zhu, Linli. Gradient Learning Algorithms for Ontology Computing. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1034648
American Medical Association (AMA)
Gao, Wei& Zhu, Linli. Gradient Learning Algorithms for Ontology Computing. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1034648
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034648