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Optimal adaptive k-means algorithm with dynamic adjustment of learning rate
TLDR
An adaptive k-means clustering algorithm that approximates an optimal clustering solution with an efficient adaptive learning rate, which renders it usable in situations where the statistics of the problem task varies slowly with time. Expand
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Optimal adaptive k-means algorithm with dynamic adjustment of learning rate
TLDR
The authors present a modification of the traditional k-means clustering algorithm that approximates an optimal clustering solution with an adaptive learning rate. Expand
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K-means competitive learning for non-stationary environments
TLDR
A modified k-means competitive learning algorithm that can perform efficiently in situations where the input statistics are changing, such as in nonstationary environments, is presented. Expand
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Adaptive k-means algorithm with error-weighted deviation measure
TLDR
The k-means algorithm can be used in multi-module networks to partition the input domain of supervised learning problems. Expand
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Real-time 3D ultrasound denoising based on adaptive regularisation Savitzky–Golay filter
TLDR
In this Letter, 3Dadaptive regularisation Savitzky–Golay (3D ARSG) filter is proposed as the real-time filter for removing speckle noise in 3D-US imaging. Expand
Dynamic adjustment of the least-mean-square step-size based on variances of weight fluctuations
We present in this paper a new method that adjusts the step-size of the LMS algorithm dynamically according to the uniformity among the variances of the fluctuations in the filter weights. This newExpand
Differential Form of Bivariate MMSE Estimator Based on Gaussian Noise
TLDR
We proposed a differential form of bivariate MMSE estimator using Pearson type VII random vectors. Expand
Analysis of Simple Neural Networks
Abstract : In a layered feet-forward network the error surface with respect to a desired goal function completely describes the potential of that network to carry out a particular task. Such an errorExpand
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