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Leave-two-out stability of ontology learning algorithm

Author

Listed:
  • Wu, Jianzhang
  • Yu, Xiao
  • Zhu, Linli
  • Gao, Wei

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.

Suggested Citation

  • Wu, Jianzhang & Yu, Xiao & Zhu, Linli & Gao, Wei, 2016. "Leave-two-out stability of ontology learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 322-327.
    • Handle: RePEc:eee:chsofr:v:89:y:2016:i:c:p:322-327
    DOI: 10.1016/j.chaos.2015.12.013
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    References listed on IDEAS

    as
    1. Jianzhang Wu & Xiao Yu & Wei Gao, 2014. "Learning rate of gradient descent multi-dividing ontology algorithm," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 28(4/5/6), pages 217-230.
    2. Wei Gao & Tianwei Xu, 2013. "Stability Analysis of Learning Algorithms for Ontology Similarity Computation," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-9, June.
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