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Sequential Classification of Probabilistic Independent Feature Vectors Based on Multilayer Perceptron

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Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

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Abstract

The paper presents methods of classification based on a sequence of feature vectors extracted from signal generated by the object. The feature vectors are assumed to be probabilistic independent. Each feature vector is separately classified by a multilayer perceptron giving a set of local classification decisions. This set of statistical independent decisions is a base for a global classification rule. The rule is derived from statistical decision theory. According to it, an object belongs to a class for which product of corresponding neural network outputs is the largest. The neural outputs are modified in a way to prevent them vanishing to zero. The performance of the proposed rule was tested in an automatic, text independent, speaker identification task. Achieved results are presented.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

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References

  1. Al-Ani, A., Deriche, M.: A new technique for combining multiple classifiers using the Dempster-Shafer theory of evidence. Journal of Artificial Intelligence Research 17, 333–361 (2002)

    MATH  MathSciNet  Google Scholar 

  2. Bauer, R., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36, 105–139 (1999)

    Article  Google Scholar 

  3. Bilmes, J., Kirchhoff, K.: Generalized rules for combination and joint training of classifiers. Pattern Analysis and Applications 6, 201–211 (2003)

    Article  MathSciNet  Google Scholar 

  4. Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)

    Google Scholar 

  5. Brock, W.A., Dechert, W.D., Scheinkman, J.A., Le Baron, B.: A test for independence based on the correlation dimension. Econometric Reviews 15, 197–235 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  6. Diks, C., Manzan, S.: Tests for Serial Independence and Linearity Based on Correlation Integrals. Studies in Nonlinear Dynamics & Econometrics, Article 2, 6(2) (2002)

    Google Scholar 

  7. Hashem, S.: Optimal linear combinations of neural networks. Neural Networks 10, 599–614 (1997)

    Article  Google Scholar 

  8. Kittler, J., Hataf, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)

    Article  Google Scholar 

  9. Kirchhoff, K., Bilmes, J.: Dynamic classifier combination in hybrid speech recognition systems using utterance-level confidence values. In: Proceedings international conference on acoustic, speech and signal processing, pp. 693–696 (1999)

    Google Scholar 

  10. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. John Wiley, New York (2004)

    Book  MATH  Google Scholar 

  11. Ripley, B.D.: Pattern Recognition and Neural Networks. University Press, Cambridge (1996)

    MATH  Google Scholar 

  12. Ruck, D.W., Rogers, S.K., Kabrisky, M., Oxley, M.E., Suter, B.W.: The multilayer perceptron as an approximation to a Bayes optimal discriminant function. IEEE Transactions on Neural Networks 1, 296–298 (1990)

    Article  Google Scholar 

  13. Walkowiak, T.: Probabilistic Neural Network for Open Set Classification. In: IV National Conference Neural Networks and their Application, Poland, Zakopane, pp. 232–237 (1999)

    Google Scholar 

  14. Walkowiak, T.: A t-Student based Neural Network for Speaker Recognition. In: Proceedings of the Fourth International Workshop Neural Networks in Applications, Germany, Magdeburg, pp. 133–139 (1999)

    Google Scholar 

  15. Walkowiak, T., Zamojski, W.: Heuristic sequential classifiers based on multilayer percepetron and probabilistic neural network. In: 2nd International Conference on Information Technology, Jordan, Amman, pp. 202–206 (2005)

    Google Scholar 

  16. Xu, L., Krzyzak, A., Suen, C.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man and Cybernetics 22, 418–435 (1992)

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Institute of Engineering Cybernetics, Wroclaw University of Technology, ul. Janiszewskiego 11/17, 50-372, Wroclaw, Poland

    Tomasz Walkowiak

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  1. Tomasz Walkowiak
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Editors and Affiliations

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

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

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

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Walkowiak, T. (2005). Sequential Classification of Probabilistic Independent Feature Vectors Based on Multilayer Perceptron. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_53

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  • DOI: https://doi.org/10.1007/11550907_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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