Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1895407 | Chaos, Solitons & Fractals | 2016 | 6 Pages |
Abstract
Ontology is a semantic analysis and calculation model, which has been applied to many subjects. Ontology similarity calculation and ontology mapping are employed as machine learning approaches. The purpose of this paper is to study the leave-two-out stability of ontology learning algorithm. Several leave-two-out stabilities are defined in ontology learning setting and the relationship among these stabilities are presented. Furthermore, the results manifested reveal that leave-two-out stability is a sufficient and necessary condition for ontology learning algorithm.
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Statistical and Nonlinear Physics
Authors
Jianzhang Wu, Xiao Yu, Linli Zhu, Wei Gao,