This Article Statistics
Viewed : 2052 Downloaded : 1669


Superiorities of Deep Extreme Learning Machines against Convolutional Neural Networks

Gökhan Altan*, Yakup Kutlu


Deep Learning (DL) is a machine learning procedure for artificial intelligence that analyzes the input data in detail by increasing neuron sizes and number of the hidden layers. DL has a popularity with the common improvements on the graphical processing unit capabilities. Increasing number of the neuron sizes at each layer and hidden layers is directly related to the computation time and training speed of the classifier models. The classification parameters including neuron weights, output weights, and biases need to be optimized for obtaining an optimum model. Most of the popular DL algorithms require long training times for optimization of the parameters with feature learning progresses and back-propagated training procedures. Reducing the training time and providing a real-time decision system are the basic focus points of the novel approaches. Deep Extreme Learning machines (Deep ELM) classifier model is one of the fastest and effective way to meet fast classification problems. In this study, Deep ELM model, its superiorities and weaknesses are discussed, the problems that are more suitable for the classifiers against Convolutional neural network based DL algorithms.


Deep Learning, Deep ELM, fast training, LUELM-AE, Hessenberg, autoencoder.

Download full text   |   How to Cite   |   Download XML Files

  • Altan, G., Kutlu, Y, Pekmezci, AÖ & Yayik, A., (2018a), Diagnosis of Chronic Obstructive Pulmonary Disease using Deep Extreme Learning Machines with LU Autoencoder Kernel, 7th International Conference on Advanced Technologies (ICAT’18), 28 April-1 May 2018, p. 618-622
  • Altan, G. & Kutlu, Y, (2018b), Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis, Natural and Engineering Sciences, 10 October 2018, ISSN: 2458-8989, Vol.3, Issue.3, pp.311–322, https://doi.org/10.28978/nesciences.468978
  • Altan, G. & Kutlu, Y, (2018c), Hessenberg Elm Autoencoder Kernel For Deep Learning, Journal of Engineering Technology and Applied Sciences, Volume 3, Issue 2, 30 August 2018, pp. 141-151, e-ISSN 2548-0391, https://doi.org/10.30931/jetas.450252.
  • Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2011, September). Convolutional neural network committees for handwritten character classification. In Document Analysis and Recognition (ICDAR), 2011 International Conference on (pp. 1135-1139). IEEE.
  • Ding, S., Zhang, N., Xu, X., Guo, L. & Zhang, J.. (2015). Deep Extreme Learning Machine and Its Application in EEG Classification, Mathematical Problems in Engineering, vol. 2015, Article ID 129021, 11 pages, 2015. https://doi.org/10.1155/2015/129021.
  • Huang, G-B, Chen, L & Siew, C-K. 2006. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw., 17(4):pp.879–92.
  • Huang, G-B & Chen, L. 2007. Convex incremental extreme learning machine. Neurocomputing. 70, pp.3056–62.
  • Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv:1404.2188.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
  • Kutlu, Y., Yayık, A., Yildirim, E., & Yildirim, S. (2017). LU triangularization extreme learning machine in EEG cognitive task classification. Neural Computing and Applications, 1-10.
  • LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. Nature, 521(7553), pp.436.
  • Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks, 8(1), 98-113.
  • Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G. (2015). A convolutional neural network cascade for face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5325-5334).
  • Liou, C.-Y., Cheng, C.-W., Liou, J.-W., & Liou, D.-R., 2014. Autoencoder for Words, Neurocomputing, vol.139, pp.84–96 (2014), doi:10.1016/j.neucom.2013.09.055
  • Tang, J., Deng, C. & Huang, G., (2016) "Extreme Learning Machine for Multilayer Perceptron," in IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 4, pp. 809-821, April 2016. doi: 10.1109/TNNLS.2015.2424995
  • Tensorflow, 2018. Convolutional Neural Networks (CNNs). World Wide Web electronic publication. https://www.easy-tensorflow.com/tf-tutorials/convolutional-neural-nets-cnns, version (11/2018).