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Comparative Study of Extreme Learning Machine and Support Vector Machine

  • Xun-Kai Wei
  • Ying-Hong Li
  • Yue Feng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

Comparative study of extreme learning machine (ELM) and support vector machine (SVM) is investigated in this paper. A cross validation method for determining the appropriate number of neurons in the hidden layer is also proposed in this paper. ELM proposed by Huang, et al [3] is a novel machine-learning algorithm for single hidden-layer feedforward neural network (SLFN), which randomly chooses the input weights and hidden-layer bias, and analytically determines the output weights optimally instead of tuning them. This algorithm tends to produce good generalization ability and obtain least experience risk simultaneously with solid foundations. Benchmark tests of a real Tennessee Eastman Process (TEP) are carried out to validate its superiority. Compared with SVM, this proposed algorithm is much faster and has better generalization performance than SVM in the case studied in this paper.

Keywords

Support Vector Machine Extreme Learn Machine Hide Neuron Feedforward Neural Network Benchmark Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Chapelle, O., Vapnik, V.N.: Model Selection for Support Vector Machines. In: Solla, S.A., Leen, T.K., Muller, K.-R. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 230–236. MIT Press, Cambridge (2000)Google Scholar
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    Vapnik, V.N.: Statistical Learning Theory, 1st edn. Wiley, New York (1998)MATHGoogle Scholar
  3. 3.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks. In: Proceedings of International Joint Conference on Neural Networks (IJCNN 2004), Budapest, Hungary, pp. 25–29 (2004)Google Scholar
  4. 4.
    Huang, G.-B.: Learning Capability and Storage Capacity of Two-hidden-layer Feedforward Networks. IEEE Transactions on Neural Networks 14(2), 274–281 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xun-Kai Wei
    • 1
  • Ying-Hong Li
    • 1
  • Yue Feng
    • 1
  1. 1.School of EngineeringAir Force Engineering UniversityXi’anChina

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