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Signal Extraction and Knowledge Discovery Based on Statistical Modeling

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Algorithmic Learning Theory (ALT 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2842))

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Abstract

In the coming post IT era, the problems of signal extraction and knowledge discovery from huge data sets will become very important. For this problem, the use of good model is crucial and thus the statistical modeling will play an important role. In this paper, we show two basic tools for statistical modeling, namely the information criteria for the evaluation of the statistical models and generic state space model which provides us with a very flexible tool for modeling complex and time-varying systems. As examples of these methods we shall show some applications in seismology and macro economics.

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Author information

Authors and Affiliations

  1. The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 106-8569, Japan

    Genshiro Kitagawa

Authors
  1. Genshiro Kitagawa
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Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Ricard Gavaldá

  2. Meme Media Laboratory, Hokkaido University Sapporo, Kita 13, Nishi 8, Kita-ku, 060-8628, Sapporo, Japan

    Klaus P. Jantke

  3.  ,  

    Eiji Takimoto

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© 2003 Springer-Verlag Berlin Heidelberg

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Kitagawa, G. (2003). Signal Extraction and Knowledge Discovery Based on Statistical Modeling. In: Gavaldá, R., Jantke, K.P., Takimoto, E. (eds) Algorithmic Learning Theory. ALT 2003. Lecture Notes in Computer Science(), vol 2842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39624-6_2

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  • DOI: https://doi.org/10.1007/978-3-540-39624-6_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20291-2

  • Online ISBN: 978-3-540-39624-6

  • eBook Packages: Springer Book Archive

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