Rangeet Mitra

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The diffusion based distributed learning approaches have been found to be a viable solution for learning over linearly separable datasets over a network. However, approaches till date are suitable for linearly separable datasets and need to be extended to scenarios in which we need to learn a non-linearity. In such scenarios, the recently proposed diffusion(More)
In a distributed network environment, the diffusion-least mean squares (LMS) algorithm gives faster convergence than the original LMS algorithm. It has also been observed that, the diffusion-LMS generally outperforms other distributed LMS algorithms like spatial LMS and incremental LMS. However, both the original LMS and diffusion-LMS are not applicable in(More)
—Recently we have proposed a maximum-likelihood iterative algorithm for estimation of parameters of the Nakagami-m distribution. This technique performs better than state of art estimation techniques for this distribution. This could be of particular use in low-data/block based estimation problems. In these scenarios, the estimator should be able to give(More)
—Iterative algorithms are ubiquitous in the field of data mining. Widely known examples of such algorithms are the least mean square algorithm, backpropagation algorithm of neural networks etc. Our contribution in this paper is an improvement upon these iterative algorithms in terms of their respective performance metrics and robustness. This improvement is(More)
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