O. B. Akan, O. B. Karli, and O. Ergul, Cognitive radio sensor networks, IEEE Network, vol.23, issue.4, pp.34-40, 2009.
DOI : 10.1109/MNET.2009.5191144

I. F. Akyildiz, I. F. Su, W. Sankarasubramaniam, Y. Cayirci, and E. , Wireless sensor networks: a survey, Wireless Sensor Networks: A Survey, pp.393-422, 2002.
DOI : 10.1016/S1389-1286(01)00302-4

M. Bradonji and L. Lazos, Graph-based criteria for spectrum-aware clustering in cognitive radio networks, Ad Hoc Networks, pp.75-94, 2012.
DOI : 10.1016/j.adhoc.2011.05.009

V. Cherkassky and Y. Ma, Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks, vol.17, issue.1, pp.113-126, 2004.
DOI : 10.1016/S0893-6080(03)00169-2

J. Kennedy and R. Eberhart, Particle swarm optimization, Proceedings of ICNN'95, International Conference on Neural Networks, pp.1942-1948, 1995.
DOI : 10.1109/ICNN.1995.488968

J. Kennedy and W. M. Spears, Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), 1998.
DOI : 10.1109/ICEC.1998.699326

H. Kuhn and A. Tucker, Nonlinear Programming, Proc. of the 2nd Berkeley Symp. on Mathematical Statistics and Probabilistics, pp.481-492, 1951.

J. Mercer, Functions of positive and negative type and their connection with the theory of integral equations, Phil. Trans. of the Royal Society A, vol.209, p.44458, 1909.

G. F. Smits and E. M. Jordan, Improved SVM regression using mixtures of kernels, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), pp.2785-2790, 2002.
DOI : 10.1109/IJCNN.2002.1007589

J. A. Suykens, L. Lukas, and J. Vandewalle, Sparse approximation using least squares support vector machines, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), pp.757-760, 2000.
DOI : 10.1109/ISCAS.2000.856439
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.7016

V. N. Vapnik, The Nature of Statistical Learning Theory, 1995.

V. N. Vapnik, Statistical Learning Theory, 1998.

K. Yau, P. Komisarczuk, and P. D. Teal, Cognitive Radio-based Wireless Sensor Networks: Conceptual design and open issues, 2009 IEEE 34th Conference on Local Computer Networks, pp.955-962, 2009.
DOI : 10.1109/LCN.2009.5355016
URL : http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5355016

O. Younis and S. Fahmy, HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks, IEEE Transactions on Mobile Computing, vol.3, issue.4, pp.366-379, 2004.
DOI : 10.1109/TMC.2004.41

H. Zhang, Z. Y. Zhang, and C. Yuen, Energy-efficient spectrum-aware clustering for cognitive radio sensor networks, Chinese Science Bulletin, vol.3, issue.28-29, pp.3731-3739, 2012.
DOI : 10.1007/s11434-012-5254-4