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Hybrid Extreme Learning Machine and Backpropagation with Adaptive Activation Functions for Classification Problems

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1351)

Abstract

This paper proposes a hybrid approach of Extreme Learning Machine with Backpropagation with adaptive activation functions for classification problems. In general, machine learning research seeks to find algorithms that can learn specific parameters through data to create increasingly accurate generalist predictive models. In some scenarios, these models become very complex, requiring great computational power for both the training stage and the predictive stage. Adaptive activation functions emerged intending to increase models’ predictive capacity, thus generating better models without increasing their complexity. We evaluate the performance of the proposal in a benchmark of ten classification problems. The results obtained show that the hybrid approach with adaptive activation functions, in general, surpasses the standard functions with the same architecture.

Keywords

  • Adaptive activation functions
  • Extreme learning machine
  • Backpropagation
  • Classification problems

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Acknowledgment

The authors acknowledge the financial support of CNPq (429639/2016-3), FAPEMIG (APQ-00334/18), and CAPES - Finance Code 001. The authors would like to thank Itaú Unibanco for hours released to its collaborator to develop this work.

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Correspondence to T. L. Fonseca .

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Fonseca, T.L., Goliatt, L. (2021). Hybrid Extreme Learning Machine and Backpropagation with Adaptive Activation Functions for Classification Problems. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_2

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