1 Abstract—The combination of the famed kernel trick and the least-mean-square (LMS) algorithm provides an interesting sample by sample update for an adaptive filter in reproducing Kernel Hilbert Spaces (RKHS), which is named here the KLMS. Unlike the accepted view in kernel methods, this ...
The combination of the famed kernel trick and the least-mean-square (LMS) algorithm provides an interesting sample-by-sample update for an adaptive filter in reproducing kernel Hilbert spaces (RKHS), which is named in this paper the KLMS. Unlike the accepted view in kernel methods, this paper...
KernelDecorrelationSignal processing algorithmsConvergenceSteady-stateCorrelationAdaptive filtersWith highly correlated input signal, the kernel least-mean-square algorithm(KLMS) always possess a low convergence rate. To overcome this problem the input signal should be decorrelated before adaptive filtering. A ...
The kernel least mean square (KLMS) algorithm is the simplest algorithm in kernel adaptive filters. However, the network growth of KLMS is still an issue for preventing its online applications, especially when the length of training data is large. The Nystr?m method is an efficient method for...
Paulo S.R. Diniz, "The Least-Mean-Square (LMS) Algorithm," Algorithms and Practical Implementation, 2008, Springer US, pp 1-54P. S. R. Diniz, "The least-mean-square ͑LMS͒ algorithm," in Adaptive Filtering: Algorithms and Practical Implementa- tion ͑Kluwer Academic, Dordrecht, ...
The spectral analysis of signals is currently either dominated by the speed–accuracy trade-off or ignores a signal’s often non-stationary character. Here we introduce an open-source algorithm to calculate the fast continuous wavelet transform (fCWT). T
As a result, we were able to estimate the air temperature distribution with an average root mean square error (RMSE) of 1.3 °C for all cases when the average RMSE of the prior information for all cases was 2.1 °C. This improvement in the RMSE indicates that this method is able to ...
A simulation study was carried out with the objective of investigating the behavior of each algorithm. The data sets were generated by considering different sample sizes and percentages of right-censored observations. Based on the root mean square error (RMSE), we identified the algorithms with the...
Anexia Internetdienstleistungs GmbH donated to the Django Software Foundation to support Django development. Donate today! DSF member of the month - Hiroki Kiyohara The start of a new blog post series, DSF member of the month, with DSF members presented each month through an interview. The first...
where (ci, cj, ck) is the center coordinate of the kernel and a is a random number to increase randomness (ranges from 0.5 to 1). Other kernels in the convolutional layers are randomly initialized with a Gaussian distribution (mean is 0, standard deviation is 1). Using our workstation ...