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Single-Layer Neural Net Competes with Multi-layer Neural Net

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Intelligent Data Engineering and Automated Learning – IDEAL 2008 (IDEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5326))

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Abstract

This paper presents a novel neural network with only one layer which can compete with multi-layer neural nets. This novel neural net is called a double-threshold single-layer neural net. The theoretical analysis and experiments show that it can demonstrate similar performance as multi-layer neural nets.

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References

  1. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  MATH  Google Scholar 

  2. Bishop, C.M.: Neural Networks and Pattern Recognition. Oxford Press, London (1995)

    Google Scholar 

  3. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Willey & Sons, Inc., New York (2002)

    MATH  Google Scholar 

  4. Cun, Y.L.: A learning scheme for asymmetric threshold networks. In: Proceedings of Cognitiva, Paris, France, vol. 85, pp. 599–604 (1985)

    Google Scholar 

  5. Hassibi, B., Stork, D.G., Wolff, G., Watanabe, T.: Optimal brain surgeon: extensions and performance comparison. In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) Advances in Neural Information Processing Systems, vol. 6, pp. 263–270. Morgan Kaufmann, San Mateo (1994)

    Google Scholar 

  6. Fahlman, S.E.: Faster-learning variations on back-propagation: An empirical study. In: Sejnowski, T.J., Hinton, G.E., Touretzky, D.S. (eds.) 1988 Connectionist Models Summer School, San Mateo, CA, Morgan Kaufmann, San Francisco (1988)

    Google Scholar 

  7. Lawrence, S., Giles, C.L.: Overfitting and Neural Networks: Conjugate Gradient and Backpropagation. In: IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 1, p. 1114 (2000)

    Google Scholar 

  8. Reed, R.: Pruning algorithms – a survey. IEEE Trans. on Neural Networks 4, 740–747 (1993)

    Article  Google Scholar 

  9. Jordanov, I., Georgieva, A.: Neural network learning with global heuristic search. IEEE Trans. on Neural Networks 18, 937–942 (2007)

    Article  Google Scholar 

  10. Peng, J.X., Li, K., Irwin, G.W.: A new Jocobian matrix for optimal learning of single-layer neural networks. IEEE Trans. on Neural Networks 19, 119–129 (2008)

    Article  Google Scholar 

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© 2008 Springer-Verlag Berlin Heidelberg

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Yang, Z.R. (2008). Single-Layer Neural Net Competes with Multi-layer Neural Net. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_65

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  • DOI: https://doi.org/10.1007/978-3-540-88906-9_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88905-2

  • Online ISBN: 978-3-540-88906-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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