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International Conference on Natural Computation

ICNC 2005: Advances in Natural Computation pp 110–118Cite as

Active Learning for Probabilistic Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3610))

Abstract

In many neural network applications, the selection of best training set to represent the entire sample space is one of the most important problems. Active learning algorithms in the literature for neural networks are not appropriate for Probabilistic Neural Networks (PNN). In this paper, a new active learning method is proposed for PNN. The method was applied to several benchmark problems.

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References

  1. Hasenjager, M., Ritter, H.: Active Learning In Neural Networks. In: Jain, L. (ed.) New Learning Techniques in Computational Intelligence Paradigms. CRC Press, Florida (2000)

    Google Scholar 

  2. RayChaudhuri, T., Hamey, L.G.C.: Minimization Of Data Collection By Active Learning. In: Proc. of the IEEE Int. Conf. Neural Networks (1995)

    Google Scholar 

  3. Takizawa, H., Nakajima, T., Kobayashi, H., Nakamura, T.: An Active Learning Algorithm Based On Existing Training Data. IEICE Trans. Inf. & Sys. E83-D (1), 90–99 (2000)

    Google Scholar 

  4. Thrun, S.: Exploration In Active Learning. In: Arbib, M. (ed.) Handbook of Brain Science and Neural Networks. MIT Press, Cambridge (1995)

    Google Scholar 

  5. Leisch, F., Jain, L.C., Hornik, K.: Cross-Validation With Active Pattern Selection For Neural Network Classifiers. IEEE Trans. Neural Networks 9(1), 35–41 (1998)

    Article  Google Scholar 

  6. Sugiyama, M., Ogawa, H.: Active Learning With Model Selection For Optimal Generalization. In: Proc. of the IBIS 2000 Workshop on Information Based Induction Science, Izu, Japan, pp. 87–92 (2000)

    Google Scholar 

  7. RayChaudhuri, T., Hamey, L.G.C.: Active Learning For Nonlinear System Identification And Control. In: Gertler, J.J., Cruz, J.B., Peshkin, M. (eds.) Proc. IFAC World Congress 1996, San Fransisco, USA, pp. 193–197 (1996)

    Google Scholar 

  8. Saar-Tsechansky, M., Provost, F.: Active Learning For Class Probability Estimation And Ranking. In: Proc. of Seventeenth International Joint Conference on Artificial Intelligence (IJCAI 2001), Seattle, WA, USA (2001)

    Google Scholar 

  9. Plutowski, M., Halbert, W.: Selecting Exemplars For Training Feedforward Networks From Clean Data. IEEE Trans. on Neural Networks 4(3), 305–318 (1993)

    Article  Google Scholar 

  10. Tong, S., Koller, D.: Active Learning For Parameter Estimation In Bayesian Networks. In: Proc. of Advances in Neural Information Processing Systems, Denver, Colorado, USA (2000)

    Google Scholar 

  11. Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20, 273–297 (1995)

    MATH  Google Scholar 

  12. Tong, S., Koller, D.: Support Vector Machine Active Learning With Applications To Text Classification. Journal of Machine Learning Research 2, 45–66 (2001)

    Article  Google Scholar 

  13. Munro, P.W.: Repeat Until Bored: A Pattern Selection Strategy. In: Moody, J., Hanson, S., Lippmann, R. (eds.) Proc. Advances in Neural Information Processing Systems (NIPS 1991), pp. 1001–1008 (1991)

    Google Scholar 

  14. Goh, T.C.: Probabilistic Neural Network For Evaluating Seismic Liquefaction Potential. Canadian Geotechnology Journal 39, 219–232 (2002)

    Article  Google Scholar 

  15. Parzen, E.: On Estimation Of A Probability Density Function And Model. Annals of Mathematical Statistics 36, 1065–1076 (1962)

    Article  MathSciNet  Google Scholar 

  16. Murphy, P.M., Aha, D.W.: UCI Repository of Machine Learning Databases. University of California, Department of Information and Computer Science (1994), Available at http://www.ics.uci.edu/~mlearn/MLRepository.html

  17. Bolat, B., Kucuk, U.: Speech/Music Classification By Using Statistical Neural Networks (In Turkish). In: Proc. of IEEE 12th Signal Processing and Communications Applications Conference (SIU 2004), Kusadasi, Turkey, pp. 227–229 (2004)

    Google Scholar 

  18. Holst, A.: The Use Of A Bayesian Neural Network Model For Classification Tasks. Ph.D. Thesis. Stockholm University (1997)

    Google Scholar 

  19. Agre, G., Koprinska, I.: Case-Based Refinement Of Knowledge Based Neural Networks. In: Albus, J., Meystel, A., Quintero, R. (eds.) Proc. of International Conference on Intelligent Systems: A Semiotic Perspective, Gaithersburg, MD, USA, pp. 221–226 (1996)

    Google Scholar 

  20. Horton, P., Nakai, K.: A Probabilistic Classification System For Predicting The Cellular Localization Sites of Proteins. Intelligent Systems in Molecular Biology 4, 109–115 (1996)

    Google Scholar 

  21. Avci, M., Yildirim, T.: Classification of Escheria Coli Bacteria By Artificial Neural Networks. In: Proc. of IEEE International Symposium on Intelligent Systems, Varna, Bulgaria, pp. 16–20 (2002)

    Google Scholar 

  22. Yang, J., Honavar, V.: A Simple Random Quantization Algorithm For Neural Network Pattern Classifiers. In: Proc. of World Congress on Neural Network, San Diego, CA, USA, pp. 223–228 (1996)

    Google Scholar 

  23. Zavrel, J.: An Empirical Re-examination Of Weighted Voting For k-NN. In: Proc. of the 7th Belgian-Dutch Conference on Machine Learning (BENELEARN 1997), Tilburg, Netherlands (1997)

    Google Scholar 

  24. Bacardit, J., Butz, M.V.: Data Minin. In: Learning Classifier Systems: Comparing XCS With GAssist. Technical Report No. 2004030. Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana, Urbana, IL, USA (2004)

    Google Scholar 

  25. Yu, L., Liu, H.: Efficient Feature Selection Via Analysis of Relevance and Redundancy. Journal of Machine Learning Research 5, 1205–1244 (2004)

    MathSciNet  Google Scholar 

  26. Yalcin, M., Yildirim, T.: Diagnosis Of Liver Disorders By Artificial Neural Networks (in Turkish). In: Proc. of IX. National Biomedical Engineering Meeting BIYOMUT 2003, Istanbul, Turkey, pp. 293–297 (2003)

    Google Scholar 

  27. Takae, T., Chikamune, M., Arimura, H., Shinohara, A., Inoue, H., Takeya, S., Uezono, K., Kawasaki, T.: Knowledge Discovery From Health Data Using Weighted Aggregation Classifiers. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 359–360. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  28. Weiss, G.M., Provost, F.: Learning When Training Data Are Costly: the Effect of Class Distribution on Tree induction. Journal of Artificial Intelligence Research 19, 315–354 (2003)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Electronics and Communications Eng., Yildiz Technical University, 34349, Besiktas, Istanbul, Turkey

    Bülent Bolat & Tülay Yıldırım

Authors
  1. Bülent Bolat
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  2. Tülay Yıldırım
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Bolat, B., Yıldırım, T. (2005). Active Learning for Probabilistic Neural Networks. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_13

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  • DOI: https://doi.org/10.1007/11539087_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28323-2

  • Online ISBN: 978-3-540-31853-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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