Abstract
The paper aims at training multilayer perceptron with different new error measures. Traditionally in MLP, Least Mean Square error (LMSE) based on Euclidean distance measure is used. However Euclidean distance measure is optimal distance metric for Gaussian distribution. Often in real life situations, data does not follow the Gaussian distribution. In such a case, one has to resort to error measures other than LMSE which are based on different distance metrics [7,8]. It has been illustrated in this paper on wide variety of well known time series prediction problems that generalized geometric and harmonic error measures perform better than LMSE for wide class of problems.
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Shiblee, M., Kalra, P.K., Chandra, B. (2009). Time Series Prediction with Multilayer Perceptron (MLP): A New Generalized Error Based Approach. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_5
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DOI: https://doi.org/10.1007/978-3-642-03040-6_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03039-0
Online ISBN: 978-3-642-03040-6
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