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Prediction Improvement via Smooth Component Analysis and Neural Network Mixing

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Artificial Neural Networks – ICANN 2006 (ICANN 2006)

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Abstract

In this paper we derive a novel smooth component analysis algorithm applied for prediction improvement. When many prediction models are tested we can treat their results as multivariate variable with the latent components having constructive or destructive impact on prediction results. The filtration of those destructive components and proper mixing of those constructive should improve final prediction results. The filtration process can be performed by neural networks with initial weights computed from smooth component analysis. The validity and high performance of our concept is presented on the real problem of energy load prediction.

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Author information

Authors and Affiliations

  1. Polska Telefonia Cyfrowa Ltd., Al. Jerozolimskie 181, 02-222, Warsaw, Poland

    Ryszard Szupiluk, Piotr Wojewnik & Tomasz Ząbkowski

  2. Warsaw School of Economics, Al. Niepodleglosci 162, 02-554, Warsaw, Poland

    Ryszard Szupiluk & Piotr Wojewnik

  3. Warsaw Agricultural University, Nowoursynowska 159, 02-787, Warsaw, Poland

    Tomasz Ząbkowski

Authors
  1. Ryszard Szupiluk
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  2. Piotr Wojewnik
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  3. Tomasz Ząbkowski
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Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

    Stefanos Kollias

  2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

    Andreas Stafylopatis

  3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

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© 2006 Springer-Verlag Berlin Heidelberg

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Szupiluk, R., Wojewnik, P., Ząbkowski, T. (2006). Prediction Improvement via Smooth Component Analysis and Neural Network Mixing. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_14

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  • DOI: https://doi.org/10.1007/11840930_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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