I am listing all the related publications together. If you know any other related article, please kindly let me know (weifeng@ieee.org).

Tutorials and Introductions

Weifeng Liu, Jose C. Principe, Simon Haykin. Kernel Adaptive Filtering: A Comprehensive Introduction, under contract with John Wiley, expected publication 2010 (About the book)

L. Bottou. Large-scale machine learning and stochastic algorithms, 2008. tutorial on NIPS 2008.

Kernel Least Mean Square Algorithm

Weifeng Liu, P. Pokharel, J. Principe, “The Kernel Least-Mean-Square Algorithm,” IEEE Transactions on Signal Processing, Volume 56, Issue 2, 2008 (pdf)

P. Pokharel, Weifeng Liu, J. Principe, “Kernel Least Mean Square Algorithm with Constrained Growth,” Signal Processing, Volume 89, Issue 3, March 2009

Weifeng Liu, P. Pokharel, J. Principe, "Recursively Adapted Radial Basis Function Networks and its Relationship to Resource Allocating Networks and Online Kernel Learning," IEEE Int. Workshop on Machine Learning for Signal Processing, 2007 (pdf)

P. Pokharel, Weifeng Liu, J. Principe, “Kernel LMS,” International Conference on Acoustics, Speech, and Signal Processing, 2006

J. Kivinen, A. Smola and R. C. Williamson. "Online learning with kernels," IEEE Transactions on Signal Processing, volume 52, issue 8, pages 2165-2176, 2004.

Kernel Affine Projection Algorithms

Weifeng Liu and J. Principe, “Kernel Affine Projection Algorithms,” EURASIP Journal on Advances in Signal Processing, vol. 2008, Article ID 784292, 12 pages, 2008. doi:10.1155/2008/784292 (pdf)

C. Richard, J. C. M. Bermudez, and P. Honeine. "Online prediction of time series data with kernels," IEEE Transactions on Signal Processing, 57(3):1058-1066, 2009.

K. Slavakis and S. Theodoridis. "Sliding window generalized kernel affine projection algorithm using projection mappings," EURASIP Journal on Advances in Signal Processing, 2008, 2008. URL http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/735351.

Kernel Recursive Least Squares Algorithm and Online Gaussian Process Regression

Y. Engel, S. Mannor and R. Meir. "The kernel recursive least-squares algorithm," IEEE Transactions on Signal Processing, volume 52, issue 8, pages 2275-2285, 2004.

L. Csato and M. Opper. "Sparse online Gaussian processes," Neural Computation, 14:641-668, 2002.

S. V. Vaerenbergh, J. Via, and I. Santamaria. "A sliding-window kernel RLS algorithm and its application to nonlinear channel identification," In Proc. International Conference on Accoustics, Speech and Signal Processing 2006, volume 5, pages 789-792, May 2006.

Kernel Extended Recursive Least Squares Algorithm and Kernel Kalman Filters

Weifeng Liu, Il Park, Yiwen Wang, J. Principe, “Extended Kernel Recursive Least Squares Algorithm,” IEEE Transactions on Signal Processing, Volume 57, Issue 10, Pages 3801-3814, October 2009 (pdf)

Weifeng Liu, J. Principe, "Extended recursive least squares algorithm in RKHS," 1st IAPR Workshop on Cognitive Information Processing, 2008 (pdf)

Sparsification, Pruning and Active Learning

Weifeng Liu, Il Park, J. Principe, "An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters," IEEE Transactions on Neural Networks, 2009 (accepted)

A. Bordes, S. Ertekin, J. Weston, and L. Bottou. "Fast kernel classifers with online and active learning," Journal of Machine Learning Research, 6:1579-1619, 2005. ISSN 1533-7928.

O. Dekel, S. Shalev-Shwartz, and Y. Singer. "The Forgetron: A kernel-based perceptron on a fixed budget," In Advances in Neural Information Processing Systems 18, pages 1342-1372, Cambridge, MA, 2006. MIT Press.

J. Quinonero-Candela and C. E. Rasmussen. "A unifying view of sparse approximate Gaussian process regression," Journal of Machine Learning Research, 6:1939-1959, 2005.

Growing and Pruning Radial Basis Function Networks

G. Huang, P. Saratchandran, and N. Sundararajan. "A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation," IEEE Transactions on Neural Networks, 16:57-67, 2005.

N. B. Karayiannis and G. W. Mi. "Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques," IEEE Transactions on Neural Networks, 8:1492-1506, 1997.

Y.-H. Cheng and C.-S. Lin. "A learning algorithm for radial basis function networks: with the capability of adding and pruning neurons," In Proceedings of IEEE International Conference on Neural Networks 1994, volume 2, pages 797-801, 1994.

J. Platt. "A resource-allocating network for function interpolation," Neural Computation, 3(2):213-225, 1991.

Correntropy Based Signal Processing

Weifeng Liu, P. Pokharel, J. Principe, “Correntropy: Properties and Applications in Non-Gaussian Signal Processing,” IEEE Transactions on Signal Processing, Vol. 55, Issue 11, 2007 (pdf)

Weifeng Liu, P. Pokharel, J. Principe, “Error Entropy, Correntropy and M-Estimation,” IEEE Int. Workshop on Machine Learning for Signal Processing, 2006 (pdf)

Weifeng Liu, P. Pokharel, J. Principe, “Correntropy: A Localized Similarity Measure,” Intl. Joint Conf. on Neural Networks, 2006 (pdf)

P. Pokharel, Weifeng Liu, J. Principe, “A Low Complexity Robust Detector in Impulsive Noise,” Signal Processing, Volume 89, Issue 10, October 2009

Kyu-Hwa Jeong, Weifeng Liu, S. Han, E. Hasanbelliu, J. Principe, “The correntropy MACE filter,” Pattern Recognition, 2009, doi:10.1016/j.patcog.2008.09.023

Ruijiang Li, Weifeng Liu, J. Principe, “A unifying criterion for blind source separation based on correntropy,” Signal Processing, Volume 87, Issue 8, Pages 1872-1881, August 2007