A Sequential Learning Resource Allocation Network for Image Processing Applications

Teich J, Wildermann S (2008)

Publication Type: Conference contribution

Publication year: 2008

Publisher: IEEE Press

Edited Volumes: Proceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008

City/Town: New York

Pages Range: 132-137

Conference Proceedings Title: Proceedings of the 8th International Conference on Hybrid Intelligent Systems

Event location: Barcelona ES

DOI: 10.1109/HIS.2008.101

Abstract

Online adaptation is a key requirement for image processing applications when used in dynamic environments. In contrast to batch learning, where retraining is required each time a new observation occurs, sequential learning algorithms offer the ability to iteratively adapt the existing classifier. In this paper, we present a neural network architecture and a fast online learning algorithm that allow to use the class of resource allocation networks for such adaptive image processing applications. The network is based on receptive fields that are processed by RBF sub-nets. The learning algorithm builds such networks online by adding new units to the sub-nets each time novel input data is observed. For this, we define a global and a local novelty criterion. Experimental results show that the proposed network outperforms existing RAN algorithms when used for face detection and recognition and is competitive with existing classifiers. © 2008 IEEE.

Authors with CRIS profile

Jürgen Teich Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Stefan Wildermann Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)

How to cite

APA:

Teich, J., & Wildermann, S. (2008). A Sequential Learning Resource Allocation Network for Image Processing Applications. In Proceedings of the 8th International Conference on Hybrid Intelligent Systems (pp. 132-137). Barcelona, ES: New York: IEEE Press.

MLA:

Teich, Jürgen, and Stefan Wildermann. "A Sequential Learning Resource Allocation Network for Image Processing Applications." Proceedings of the 8th International Conference on Hybrid Intelligent Systems, Barcelona New York: IEEE Press, 2008. 132-137.

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