Abstract
Learning with adaptivity is a key issue in many nowadays applications. The most important aspect of such an issue is incremental learning (IL). This latter seeks to equip learning algorithms with the ability to deal with data arriving over long periods of time. Once used during the learning process, old data is never used in subsequent learning stages. This paper suggests a new IL algorithm which generates categories. Each is associated with one class. To show the efficiency of the algorithm, several experiments are carried out.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bouchachia, A.: On Adaptive Learning. In: Proc. of the 6th International Joint Conference on Recent Advances in Soft Computing, July 10-12, pp. 30–35 (2006)
Bouchachia, A., Mittermeir, R.: Towards Fuzzy Incremental Classifiers. Soft Computing (2006) (in press)
Grossberg, S.: Nonlinear neural networks: Principles, mechanism, and architectures. Neural Networks 1, 17–61 (1988)
Kohonen, T.: Self-organizing Maps. Springer, Berlin (1997)
Merz, J., Murphy, P.: UCI repository of machine learning databases (1996), http://www.ics.uci.edu/-learn/MLRepository.html
Xu, L., Krzyzak, A., Oja, E.: Rival Penalized Competitive Learning for Clustering Analysis, RBF Net, and Curve Detection. IEEE Trans. on Neural Networks 4(4), 636–649 (1993)
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
Bouchachia, A. (2006). Learning with Incrementality. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_16
Download citation
DOI: https://doi.org/10.1007/11893028_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
eBook Packages: Computer ScienceComputer Science (R0)