Quantized Kernel Recursive Least Squares Algorithm

In a recent paper, we developed a novel quantized kernel least mean square algorithm, in which the input space is quantized (partitioned into smaller regions) and the network size is upper bounded by the quantization codebook size (number of the regions). In this paper, we propose the quantized kern...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 24; no. 9; pp. 1484 - 1491
Main Authors:	Chen, Badong, Zhao, Songlin, Zhu, Pingping, Principe, Jose C.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.09.2013 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Applied sciences Bioinformatics Coding, codes Computer science; control theory; systems Computer simulation Controllability Data processing. List processing. Character string processing Exact sciences and technology Information, signal and communications theory Kernel Kernel recursive least squares (KRLS) Kernels Learning systems Least squares method Manganese Memory organisation. Data processing On-line systems Partitioning algorithms quantization Quantization (signal) quantized kernel recursive least squares (QKRLS) Recursive Regression Signal and communications theory Software sparsification Telecommunications and information theory Vector quantization Cluster analysis Monte Carlo method Vector quantization Updating Regression analysis Vector method Recursive algorithm Modeling Kernel method Upper bound Codebook Kernel recursive least squares (KRLS) Least squares method Signal quantization Optimal solution sparsification Sparse representation quantized kernel recursive least squares (QKRLS) quantization
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!