1 |
T. Ogunfunmi and M. Deb, "On the PDF estimation for information theoretic learning for neural networks," Proceedings of APSIPA-ASC2018, Honolulu, USA, pp. 1215-1221, Nov. 2018. DOI: https://doi.org/10.23919/APSIPA.2018.8659642
DOI
|
2 |
L. Chen, P. Honeine, "Correntropy-based robust multilayer extreme learning machines," Pattern Recognition, Elsevier, vol. 84, pp. 357-370, Dec. 2018. DOI: https://doi.org/10.1016/j.patcog.2018.07.011
DOI
|
3 |
N. Kim and G. Lee, "Performance enhancement of algorithms based on error distributions under impulsive noise," JICS, vol. 19, pp. 49-56, June, 2018. DOI: https://doi.org/10.7472/jksii.2018.19.3.49
DOI
|
4 |
J. Principe, Information Theoretic Learning, Springer, New York, 2010, pp. 103-140. DOI: https://doi.org/10.1007/978-1-4419-1570-2
|
5 |
N. Kim, "Robustness to Impulsive Noise of Algorithms based on Cross-Information Potential and Delta Functions," Journal of Internet Computing and Services, vol. 17, pp. 1-6, Dec. 2017. DOI: https://doi.org/10.7472/jksii.2016.17.2.11
DOI
|
6 |
N. Kim, "A study on kernel size adaptation for correntropy-based learning methods," Journal of the Korea Academia-Industrial Cooperation Society, in press, 2021.
|
7 |
H. Radmanesh, M. Hajiabadi, "Recursive maximum correntropy learning algorithm with adaptive kernel size," IEEE Trans. Circuits and Systems, vol. 65, pp. 958-963, July 2018. DOI: https://doi.org/10.1109/tcsii.2017.2778038
DOI
|
8 |
E. Parzen, "On the estimation of a probability density function and the mode," Ann. Math. Stat. vol. 33, p. 1065, 1962. DOI: https://doi.org/10.1214/aoms/1177704472
DOI
|
9 |
J. Proakis, Digital Communications, McGraw-Hill, NY, 1989, pp 438-439. ISBN10:0070517266
|
10 |
I. Santamaria, P. Pokharel, and J. Principe, "Generalized correlation function: Definition, properties, and application to blind equalization," IEEE Trans. Signal Processing, vol. 54, pp. 2187-2197, June 2006. DOI: https://doi.org/10.1109/tsp.2006.872524
DOI
|