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A probability estimation based criteria for model evaluation

  • Part VII: Predictions, Forecasting, and Monitoring
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

We develop a criteria based on the estimation of the joint probability density function (pdf) of the input and the error, and on the pdf of the input. It is made to decide when the couple input/model no longer fit together. The estimation of the pdf is made through a Probabilistic Radial Basis Function Network (PRBFN), which can also be used to estimate the given task. We compare the results when using a dedicated network, or when extracting the density value directly from the network which estimates the input-output mapping. We show the results of our approach on an electrical load of Spain.

The work of T. Czernichow was enabled by the Lavoisier grant of the french ministry of foreign affairs.

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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Czemichowl, T., Muñoz, A. (1997). A probability estimation based criteria for model evaluation. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020288

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  • DOI: https://doi.org/10.1007/BFb0020288

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  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

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