Stochastic Behavior Analysis of the Gaussian Kernel Least-Mean-Square Algorithm
نویسندگان
چکیده
منابع مشابه
Mean square convergence analysis for kernel least mean square algorithm
In this paper, we study the mean square convergence of the kernel least mean square (KLMS). The fundamental energy conservation relation has been established in feature space. Starting from the energy conservation relation, we carry out the mean square convergence analysis and obtain several important theoretical results, including an upper bound on step size that guarantees the mean square con...
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A simple, yet powerful, learning method is presented by combining the famed kernel trick and the least-mean-square (LMS) algorithm, called the KLMS. General properties of the KLMS algorithm are demonstrated regarding its well-posedness in very high dimensional spaces using Tikhonov regularization theory. An experiment is studied to support our conclusion that the KLMS algorithm can be readily u...
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The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2012
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2012.2186132