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Towards the Verification and Validation of Online Learning Adaptive Systems

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Software Engineering with Computational Intelligence

Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 731))

Abstract

Online Adaptive Systems in general, and learning neural nets in particular cannot be validated using traditional verification and validation techniques, because they evolve over time, and past learning data influences their behavior. In this paper we discuss a framework for reasoning about online adaptive systems, and see how this framework can be used to perform V\&V on such systems.

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Author information

Authors and Affiliations

  1. College of Computing Science, New Jersey Inst. of Technology, Newark, NJ, 07102, USA

    Ali Mili

  2. Lane CSEE Department, West Virginia University, Morgantown, WV, 26506-6109, USA

    Bojan Cukic & Yan Liu

  3. School of Engineering, University of Tunis II, Belvedere, 1002, Tunisia

    Rahma Ben Ayed

Authors
  1. Ali Mili
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  2. Bojan Cukic
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  3. Yan Liu
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  4. Rahma Ben Ayed
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Editor information

Editors and Affiliations

  1. Florida Atlantic University, USA

    Taghi M. Khoshgoftaar

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© 2003 Springer Science+Business Media New York

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Mili, A., Cukic, B., Liu, Y., Ayed, R.B. (2003). Towards the Verification and Validation of Online Learning Adaptive Systems. In: Khoshgoftaar, T.M. (eds) Software Engineering with Computational Intelligence. The Springer International Series in Engineering and Computer Science, vol 731. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0429-0_7

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  • DOI: https://doi.org/10.1007/978-1-4615-0429-0_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-5072-9

  • Online ISBN: 978-1-4615-0429-0

  • eBook Packages: Springer Book Archive

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