Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from VLDB 2026 / reviewers in the wild / expert

Moshe Tarrab

dblp:46/4263 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 1988
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Physical-layer communications · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications › signal processing for communications
adaptive filtering		 0.011988
Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data · IEEE Trans. Inf. Theory 1988
Physical-layer communications › signal processing for communications › adaptive filtering
LMS algorithm		 0.011988
Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data · IEEE Trans. Inf. Theory 1988

Methods — techniques the papers use, named apart from their topics

stochastic approximation · 0.0mean-square analysis · 0.0
YearPublicationVenuePosition
1988 Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data
abstract
It is demonstrated that the normalized least mean square (NLMS) algorithm can be viewed as a modification of the widely used LMS algorithm. The NLMS is shown to have an important advantage over the LMS, which is that its convergence is independent of environmental changes. In addition, the authors present a comprehensive study of the first and second-order behavior in the NLMS algorithm. They show that the NLMS algorithm exhibits significant improvement over the LMS algorithm in convergence rate, while its steady-state performance is considerably worse.>
Moshe Tarrab, Arie Feuer
 IEEE Trans. Inf. Theory1