Abstract
Recently, Extreme Learning Machine (ELM) has drawn an increasing attention, Due to its fast and good generalization ability. This paper proposes a new learning method for Extreme Learning Machine based wavelet and deep architecture. We have applied a composite wavelet activation function at the hidden nodes of ELM and the learning is done by a Deep Extreme Learning Machine. To evaluate the performance of our approach we have used a standard benchmark dataset for multi-class image classification (MNIST). Results show that our approach offers a significantly better performance relative to others approaches.
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Yahia, S., Said, S., Zaied, M. (2020). Deep Wavelet Extreme Learning Machine for Data Classification. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019). CISIS ICEUTE 2019 2019. Advances in Intelligent Systems and Computing, vol 951. Springer, Cham. https://doi.org/10.1007/978-3-030-20005-3_11
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