Abstract
A novel learning algorithm for pattern association in a recurrent neural network is proposed in this paper. Unlike the conventional model in which the memory is stored in an attractive fixed point at discrete location in state space, the dynamics of the proposed learning algorithm represents memory as a line of attraction. The region of convergence at the line of attraction is defined by the statistical characteristics of training data. The performance of the learning algorithm is compared with Bayesian model in experiments on skin color segmentation.
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Seow, MJ., Asari, V.K. (2004). Recurrent Network as a Nonlinear Line Attractor for Skin Color Association. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_143
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DOI: https://doi.org/10.1007/978-3-540-28647-9_143
Publisher Name: Springer, Berlin, Heidelberg
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