Abstract
Performance of hierarchical classifiers depends on two aspects – the performance of the individual classifiers, and the design of the architecture. In this paper, we present a scheme for designing hybrid hierarchical classifiers under user specified constraints on time and space.
Chapter PDF
Similar content being viewed by others
References
Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multi-class classification. In: Advances in NIPS-12, pp. 547–553 (2000)
Kumar, S., Ghosh, J., Crawford, M.: A hierarchical multiclassifier system for hyperspectral data analysis. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 270–278. Springer, Heidelberg (2000)
Pavan Kumar, M.N.S.S.K., Jawahar, C.V.: On improving design of multiclass clasifiers. In: Proceedings of the 5th International Conference on Advances in Pattern Recognition, pp. 109–112 (2003)
Dong, M., Li, Y., Kothari, R.: Theoretical results on a measure of classification complexity. In: Proceedings of 5th International Conference on Advances in Pattern Recognition, pp. 85–88 (2003)
Hettich, S., Blake, C., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kumar, M.N.S.S.K.P., Jawahar, C.V. (2005). Design of Hierarchical Classifier with Hybrid Architectures. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_39
Download citation
DOI: https://doi.org/10.1007/11590316_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
Online ISBN: 978-3-540-32420-1
eBook Packages: Computer ScienceComputer Science (R0)