Abstract
Constructive neural network algorithms provide optimal ways to determine the architecture of a multi layer perceptron network along with learning algorithms for determining appropriate weights for pattern classification problems. In this paper the possibility of developing a novel Constructive Neural Network architecture with improved adaptive learning strategy is proposed and analyzed. The new Multi category Tiling Constructive Neural Network architecture and the existing Tiling architecture are tested on machine learning datasets. The performance of the new learning strategy on Multi Category Tiling architecture was found to be comparatively better than when applied on the existing Tiling architecture.
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Sridhar, S.S., Ponnavaikko, M. (2012). A Novel Constructive Neural Network Architecture Based on Improved Adaptive Learning Strategy for Pattern Classification. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 130. Springer, India. https://doi.org/10.1007/978-81-322-0487-9_41
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DOI: https://doi.org/10.1007/978-81-322-0487-9_41
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