Abstract
A general approach is developed to learn the conditional probability density for a noisy time series. A universal architecture is proposed, which avoids difficulties with the singular low-noise limit. A suitable error function is presented enabling the probability density to be learnt. The method is compared with other recently developed approaches, and its effectiveness demonstrated on a time series generated from a non-trivial stochastic dynamical system.
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References
Allen DW and Taylor JG, Learning Time Series by Neural Networks, Proc ICANN (1994), ed Marinaro M and Morasso P, Springer, pp529–532.
Neuneier R, Hergert F, Finnoff W and Ormoneit D, Estimation of Conditional Densities: A Comparison of Neural Network Approaches, Proc ICANN (1994), ed Marinaro M and Morasso P, Springer, pp689–692.
Papoulis, A., Probability, Random Variables and Stochastic Processes, McGraw-Hill (1984).
Srivastava AN and Weigend AS, Computing the Probability Density in Connectionist Regression, Proc ICANN (1994), ed Marinaro M and Morasso P, Springer, pp685–688.
Weigend AS and Nix DA, Predictions with Confidence Intervals (Local Error Bars), Proc ICONIP (1994), ed Kim M-W and Lee S-Y, Korea Advanced Institute of Technology, pp847–852.
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© 1997 Springer Science+Business Media New York
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Husmeier, D., Allen, D., Taylor, J.G. (1997). A Universal Approximator Network for Learning Conditional Probability Densities. In: Ellacott, S.W., Mason, J.C., Anderson, I.J. (eds) Mathematics of Neural Networks. Operations Research/Computer Science Interfaces Series, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6099-9_32
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DOI: https://doi.org/10.1007/978-1-4615-6099-9_32
Publisher Name: Springer, Boston, MA
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Online ISBN: 978-1-4615-6099-9
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