Journal ArticleDOI
Kernel least mean square algorithm with constrained growth
TLDR
A new efficient methodology for constraining the increase in length of a radial basis function (RBF) network resulting from the kernel LMS algorithm without significant sacrifice on performance is proposed.About:
This article is published in Signal Processing.The article was published on 2009-03-01. It has received 44 citations till now. The article focuses on the topics: Variable kernel density estimation & Radial basis function kernel.read more
Citations
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Journal ArticleDOI
Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning
Zhixiong Li,Rui Liu,Dazhong Wu +2 more
TL;DR: In this paper, a blind source separation method is used to separate source signals from noise and an extended principal component analysis is used for dimensionality reduction, which can be used to classify tool wear conditions with high accuracy.
Journal ArticleDOI
Adaptive kernel principal component analysis
TL;DR: An adaptive kernel principal component analysis (AKPCA) method, which has the flexibility to accurately track the kernel principal components (KPC), is presented and yields improvements in terms of both computational speed and approximation accuracy.
Proceedings ArticleDOI
Online learning with kernels: Overcoming the growing sum problem
TL;DR: This paper approximates kernel evaluations using finite dimensional inner products in a randomized feature space to apply to the Kernel Least Mean Square algorithm, that has recently been proposed as a non-linear extension to the famed LMS algorithm.
Journal ArticleDOI
Kernel Association for Classification and Prediction: A Survey
TL;DR: This survey outlines the latest trends and innovations of a kernel framework for big data analysis and provides a useful overview of this evolving field for both specialists and relevant scholars.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Adaptive Filter Theory
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
BookDOI
Density estimation for statistics and data analysis
TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.