Table of Contents Author Guidelines Submit a Manuscript
Journal of Electrical and Computer Engineering
Volume 2017, Article ID 6168207, 2 pages

Machine Intelligence in Signal Sensing, Processing, and Recognition

1College of Communication Engineering, Chongqing University, Chongqing, China
2Chair of Mathematics, IT Fundamentals and Education Technologies Applications, University of Information Technology and Management in Rzeszow, Rzeszow, Poland
3Faculty of Science and Technology, University of Macau, Taipa, Macau
4North China Electric Power University, Baoding, China
5INFN-Laboratori Nazionali di Frascati, Rome, Italy

Correspondence should be addressed to Lei Zhang; nc.ude.uqc@gnahziel

Received 25 July 2017; Accepted 25 July 2017; Published 6 September 2017

Copyright © 2017 Lei Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. View at Publisher · View at Google Scholar
  2. G.-B. Huang, “What are extreme learning machines? Filling the gap between Frank Rosenblatt's dream and John Von Neumann's puzzle,” Cognitive Computation, vol. 7, no. 3, pp. 263–278, 2015. View at Publisher · View at Google Scholar
  3. L. Zhang, W. Zuo, and D. Zhang, “LSDT: latent sparse domain transfer learning for visual adaptation,” IEEE Transactions on Image Processing, vol. 25, no. 3, pp. 1177–1191, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  4. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013. View at Publisher · View at Google Scholar · View at Scopus