Maei, H.R., Szepesvari, C., Bhatnagar, S., Precup, D., Silver, D. and Sutton, R.S. (2009) Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. Proceedings of the 23rd Annual Conference on Neural Information Processing Systems (NIPS’09), Vancouver, 7-10 December 2009.

has been cited by the following article:

• TITLE: Application of Machine-Learning Based Prediction Techniques in Wireless Networks

  AUTHORS: Gitanjali Bhutani

  KEYWORDS: Wireless Networks, Prediction, Machine Learning, Ubiquitous Communication, Pervasive Computing

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.7 No.5, May 19, 2014

  ABSTRACT: Wireless networks are key enablers of ubiquitous communication. With the evolution of networking technologies and the need for these to inter-operate and dynamically adapt to user requirements, intelligent networks are the need of the hour. Use of machine learning techniques allows these networks to adapt to changing environments and enables them to make decisions while continuing to learn about their environment. In this paper, we survey the various problems of wireless networks that have been solved using machine-learning based prediction techniques and identify additional problems to which prediction can be applied. We also look at the gaps in the research done in this area till date.