Kernel Adaptive Filtering/Online
listing all the related publications together. If you know any other
related article, please kindly let me know (firstname.lastname@example.org).
Tutorials and Introductions
Weifeng Liu, Jose C.
Principe, Simon Haykin.
Kernel Adaptive Filtering:
A Comprehensive Introduction, John Wiley, 2010
(About the book)
L. Bottou. Large-scale machine learning and
stochastic algorithms, 2008. tutorial on NIPS 2008.
Kernel Least Mean Square
Weifeng Liu, P. Pokharel, J. Principe, The
Kernel Least-Mean-Square Algorithm, IEEE Transactions on Signal
Processing, Volume 56, Issue 2, 2008 (pdf)
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)
Weifeng Liu, J. Principe, Kernel
LMS, International Conference on Acoustics, Speech, and Signal
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.
Weifeng Liu and J. Principe, Kernel
Affine Projection Algorithms, EURASIP Journal on Advances in Signal
Processing, vol. 2008, Article ID 784292, 12 pages, 2008.
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.
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.
Recursive Least Squares
Algorithm and Kernel
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)
L. Ralaivola, F.
d'Alche-Buc. "Time series filtering, smoothing and learning using the
kernel Kalman filter," Proceedings. 2005 IEEE International Joint
Conference on Neural Networks, pages 1449-1454, 2005.
Sparsification, Pruning and
Weifeng Liu, Il Park, J. Principe, "An Information Theoretic Approach of Designing Sparse
Kernel Adaptive Filters," IEEE Transactions on Neural Networks, 2009
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
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
J. Platt. "A resource-allocating
network for function interpolation," Neural Computation, 3(2):213-225,
Correntropy Based Signal
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,
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
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