Abstract
A new supervised neural network architecture has been introduced, called Supervised ART-I. It has the accuracy of Fuzzy ARTMAP in classifying of both binary and analog arbitrary multi-valued input patterns. However, it is quicker in learning and classifying, has fewer parameters and requires less memory, due to its simple architecture.
The Supervised ART-I has been built from a single Fuzzy ART, instead of a pair of them as in Fuzzy ARTMAP.
During the training phase, when the tag of the winning node matches the class code, the node will be trained. If not, class correction is done. If non of the committed nodes is able to represent the input, a new node is committed and it is tagged with the current class code.
During the testing phase, if the winning node passes the vigilance parameter (\(\overline \rho \)), its tag represents the class of the input, if not the network fails to classify the current input.
The architecture, learning, and testing of the network have been discussed. The full algorithm has been listed.
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References
Carpenter, G. A., Gaja, M. N., Gapa, S., and Woodcok, C. E., 1997, “ART neural networks for remote sensing: vegetation classification from landsat TM and terrain data”, IEEE Trans. Geosci. Remote Sensing, Vol. 35, pp. 308–325.
Carpentar, G. A., Grossberg, S., and Renold, J. H., 1991, “ARTMAP: Supervised real-time learning and classification of nonstationary data by self-organizing neural network,” Neural Network, Vol. 4, pp. 565–588.
Tzeng, Y. C., Chen, K. S., Kao, W. L., and Fung, A. K., 1994, “A dynamic learning neural network for remote sensing applications,” IEEE Trans. Geosci. Remote Sensing, Vol. 32, pp. 1096–1102.
Salv, Y., and Tilton, J., 1993, “Classification of multispectral image data by the binary diamond neural network and by non parameteric, pixil-by-pixel methods,” IEEE Trans. Geosci. Remote Sensing, Vol. 31, pp: 606–617.
Benediktsson, J. A., Swain, P. H., and Ersoy, O. K., 1990, “Neural network approaches versus statistical methodes in classification of multisource remote sensing data,” IEEE Trans. Geosci. Remote Sensing, Vol. 28, pp. 540–552.
Carpenter, G. A., Grossberg, S., Markuzan, N., Reynold, J. H., and Rosen, D. B., 1992, “FUZZY ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps,” IEEE Trans. On Neural Network, Vol. 3, pp. 698–882.
Gan, K. W., and Lua, K. T., 1992, “Chinese character classification using adaptive resonance network,” Pattern Recog., Vol. 25, pp. 877–882.
Shih, F. X., Moh, J., and Chang, F., 1992, “A new art-based neural architecture for pattern classification and image enhancement without prior knoweldge,” Pattern Recog., Vol. 25, pp. 533–542.
Bebis, G. N., and Papadourakis G. M., 1992, “Object recognition using invariant object bounddary representations and nueral network models,” Pattern Recog., Vol. 25, pp. 25–44.
Baraldi, A., and Parmiggiani, F., 1995, “A neural network for unsupervised categorization for multivalued input patterns. An application to satellite image clustering,” IEEE Trans. Geosci. Remote Sensing, Vol. 33, pp. 305–316.
Moore, B., 1989, “ART1 and patterns clustering,” in proc. 1988 connectionist models Summer School, D. Touretzky, G. Hintoon, and T. Sejnowski, Eds, San Mateo, CA: Morgan Kaufmann, pp. 174–185.
Carpenter, G. A., Grossberg, S., and Rosen, D. B., 1991, “Fuzzt ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system,” Neural Networks, Vol. 4, pp: 759–771.
Al-Rawi, K. R., Gonzalo, C., and Arquero, A., 1999, “Supervised ART-II: A new neural network architecture, with quicker learning algorithm, for classifying multi-valued input patterns,” to be presented in the 7th conference of the European Symposium on Artificial Neural Network ESANN, Bruges, Belgium.
Al-Rawi, K. R., Gonzalo, C., and Martinez, E., 1999, “Supervised ART-II for classifying Landsat TM images,” to be presented in the 19th conference of the European Association of Remote Sensing Laboratories EARSeL, LATUV, University of Valladolid, Valladolid, Spain.
Al-Rawi, K. R., and Casanova, J. L., 1999, “A neural network approach for automatic monitoring of forest fires with NOAA-AVHRR imagery. Part-I: mapping burned areas,” sent for publication to the Int. J. of Remote Sensing.
Al-Rawi, K. R., Casanova, J. L., and Calle, A., 1999, “A neural network approach for automatic monitoring of forest fires with NOAA-AVHRR imagery. Part-II: fires detection,” sent for publication to the Int. J. of Remote Sensing.
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Al-Rawi, K.R. (1999). Supervised ART-I: A new neural network architecture for learning and classifying multi-valued input patterns. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098234
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DOI: https://doi.org/10.1007/BFb0098234
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