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Image Processing and Communications Challenges 4 pp 201–208Cite as

On the Bounds on Optimal Bayes Error in the Task of Multiple Data Sources

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 184))

Summary

The paper considers the problem of pattern recognition when we have multiple data sources. We assume that for each data source there are estimated parameters of statistical distributions. The model of classification is primarily based on the Bayes rule and secondarily on the notion of interval-valued fuzzy sets. The set of possible class-conditional probability density functions is represented by an interval-valued fuzzy set. We consider the case where the uncertainty concerns the mean of Gaussian pdf. In the paper the bound on the optimal Bayess error is presented for a full probabilistic information.

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Authors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Robert Burduk

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  1. Robert Burduk
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Correspondence to Robert Burduk .

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Editors and Affiliations

  1. , Institute of Telecommunications, University of Technology & Life Sciences, ul.S.Kaliskiego 7, Bydgoszcz, 85-791, Poland

    Ryszard S. Choraś

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Burduk, R. (2013). On the Bounds on Optimal Bayes Error in the Task of Multiple Data Sources. In: Choraś, R. (eds) Image Processing and Communications Challenges 4. Advances in Intelligent Systems and Computing, vol 184. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32384-3_25

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  • DOI: https://doi.org/10.1007/978-3-642-32384-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32383-6

  • Online ISBN: 978-3-642-32384-3

  • eBook Packages: EngineeringEngineering (R0)

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