Abstract
Prototype classifiers are one of the simplest and most intuitive approaches in pattern classification. However, they need careful positioning of prototypes to capture the distribution of each class region. Classical methods, such as learning vector quantization (LVQ), are sensitive to the initial choice of the number and the locations of the prototypes. To alleviate this problem, a new method is proposed that represents each class region by a set of compact hyperspheres. The number of hyperspheres and their locations are determined by setting up the problem as a set of quadratic optimization problems. Experimental results show that the proposed approach significantly beats LVQ and Restricted Coulomb Energy (RCE) in most performance aspects.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification. IEEE Trans. Pattern Analysis and Machine Intelligence. 18(6), 607–616 (1996)
Reilly, D., Cooper, L., Elbaum, C.: A neural model for category learning. Biological Cybernetics 45, 35–41 (1982)
Hudak, M.J.: RCE Classifiers: Theory and Practice. Cybernetics and Systems: An International Journal 23, 483–515 (1992)
Tsumura, N., Itoh, K., Ichioka, Y.: Reliable classification by double hyperspheres in pattern vector space. Pattern Recognition 28, 1621–1626 (1995)
Kositsky, M., Ullman, S.: Learning Class Regions by the Union of Ellipsoids. In: Proc. 13th International Conference on Pattern Recognition (ICPR), vol. 4, pp. 750–757. IEEE Computer Society Press, Los Alamitos (1996)
Atiya, A., Hashem, S., Fayed, H.: New hyperspheres for pattern classification. In: Proc. 1st International Computer Engineering Conference (ICENCO-2004), Cairo, Egypt, pp. 258–263 (2004)
Wright, S.J.: Primal-Dual Interior-Point Methods. SIAM Publications, Philadelphia (1997)
Khachiyan, L.G.: Rounding of polytopes in the real number model of computation. Mathematics of Operations Research 21, 307–320 (1996)
Kohonen, T.: Self-organization and Associative Memory, 3rd edn. Springer, Heidelberg (1989)
Hastie, T., Tisbshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
Zhang, H., Sun, G.: Optimal reference subset selection for nearest neighbor classification by tabu search. Pattern Recognition 35, 1481–1490 (2002)
Prechelt, L.: Proben1. A Set of Neural-Network Benchmark Problems. Univ. Karlsruhe, Germany (1994), available FTP: ira.uka.de/pub/neuron/proben1.tar.gz
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Atiya, A., Hashem, S., Fayed, H. (2006). Pattern Classification Using a Set of Compact Hyperspheres. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_13
Download citation
DOI: https://doi.org/10.1007/11893257_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
eBook Packages: Computer ScienceComputer Science (R0)