Introducing the Theory of Probabilistic Hierarchical Learning for Classification-Reference-Cited by-同舟云学术

Introducing the Theory of Probabilistic Hierarchical Learning for Classification

Published:2019 Issue: Volume: Page:628-641
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Ursani Ziauddin^ORCID,Dicks Jo^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-22999-3_54

Reference17 articles.

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3. Chen, Y., Crawford, M.M., Ghosh, J.: Integrating support vector machines in a hierarchical output space decomposition framework. In: Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, vol. 2, pp. 949–952 (2004)

4. Freitas, C.O.A., Oliveira, L.S., Aires, S.B.K., Bortolozzi, F.: Metaclasses and zoning mechanism applied to handwriting recognition. J. Univ. Comput. Sci. 14(2), 211–223 (2008)

5. Ursani, Z., Corne, D.W.: Use of reliability engineering concepts in machine learning for classification. In: 4th International Conference on Soft Computing & Machine Intelligence (IEEE), (ISCMI 2017), Mauritius (2017)

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