Abstract
The training strategy used in connectionist learning has not received much attention in the literature. We suggest a new strategy for backpropagation learning, increased complexity training, and show experimentally that it leads to faster convergence compared to both the conventional training strategy using a fixed set, and to combined subset training. Increased complexity training combined with an incremental increase in the success ratio required on the training set produced even quicker convergence.
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© 1993 Springer-Verlag Berlin Heidelberg
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Cloete, I., Ludik, J. (1993). Increased complexity training. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_158
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DOI: https://doi.org/10.1007/3-540-56798-4_158
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