Abstract
The text below describes a short introduction to extreme learning machines (ELM) enlightened by new developed applications. It also includes an introduction to deep belief networks (DBN), noticeably tuned into the pattern recognition problems. Essentially, the deep belief networks learn to extract invariant characteristics of an object or, in other words, an DBN shows the ability to simulate how the brain recognizes patterns by the contrastive divergence algorithm. Moreover, it contains a strategy based on both the kernel (and neural) extreme learning of the deep features. Finally, it shows that the DBN-ELM recognition rate is competitive (and often better) than other successful approaches in well-known benchmarks. The results also show that the method is extremely fast when the neural based ELM is used.
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References
Bengio, Y.: Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1), 1–127 (2009)
Hinton, G.: A practical guide to training Restricted Boltzmann Machines. Tech. rep., Dep. of Computer Science, University of Toronto (2010)
Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)
Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B 42(2), 513–529 (2012)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: A new learning scheme of feedforward neural networks. In: IEEE International Joint Conference on Neural Networks, pp. 985–990 (2004)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: Theory and applications. Neurocomputing 70, 489–501 (2006)
Le Roux, N., Bengio, Y.: Representational power of restricted boltzmann machines and deep belief networks. Neural Comput. 20, 1631–1649 (2008)
Lopes, N., Ribeiro, B.: GPUMLib: An efficient open-source GPU machine learning library. International Journal of Computer Information Systems and Industrial Management Applications 3, 355–362 (2011)
Lu, B., Wang, G., Yuan, Y., Han, D.: Semantic concept detection for video based on extreme learning machine. Neurocomputing 102, 176–183 (2013)
Ranzato, M., Boureau, Y., LeCun, Y.: Sparse feature learning for deep belief networks. In: Advances in Neural Information Processing Systems, vol. 20 (2007)
Shi, L.C., Lu, B.L.: EEG-based vigilance estimation using extreme learning machines. Neurocomputing 102, 135–143 (2013)
Wu, S., Wang, Y., Cheng, S.: Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system. Neurocomputing 102, 163–175 (2013)
Yeu, C.W., Lim, M.H., Huang, G.B., Agarwal, A., Ong, Y.S.: A new machine learning paradigm for terrain reconstruction. IEEE Geoscience and Remote Sensing Letters 3(3), 382–386 (2006)
Yu, K., Xu, W., Gong, Y.: Deep learning with kernel regularization for visual recognition. In: Neural Information Processing Systems, NIPS (2009)
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Ribeiro, B., Lopes, N. (2013). Extreme Learning Classifier with Deep Concepts. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_23
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DOI: https://doi.org/10.1007/978-3-642-41822-8_23
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