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Discovering Ecosystem Models from Time-Series Data

  • Dileep George
  • Kazumi Saito
  • Pat Langley
  • Stephen Bay
  • Kevin R. Arrigo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

Ecosystem models are used to interpret and predict the interactions of species and their environment. In this paper, we address the task of inducing ecosystem models from background knowledge and time-series data, and we review IPM, an algorithm that addresses this problem. We demonstrate the system’s ability to construct ecosystem models on two different Earth science data sets. We also compare its behavior with that produced by a more conventional autoregression method. In closing, we discuss related work on model induction and suggest directions for further research on this topic.

Keywords

Generic Process Autoregressive Model Ecosystem Model Modeling Task Minimum Description Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Dileep George
    • 1
  • Kazumi Saito
    • 2
  • Pat Langley
    • 1
  • Stephen Bay
    • 1
  • Kevin R. Arrigo
    • 3
  1. 1.Computational Learning Laboratory, CSLIStanford UniversityStanfordUSA
  2. 2.NTT Communication Science LaboratoriesSoraku, KyotoJapan
  3. 3.Department of GeophysicsStanford UniversityStanfordUSA

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