N
Nathan S. Netanyahu
Researcher at Bar-Ilan University
Publications - 150
Citations - 12080
Nathan S. Netanyahu is an academic researcher from Bar-Ilan University. The author has contributed to research in topics: Image registration & Deep learning. The author has an hindex of 27, co-authored 144 publications receiving 11131 citations. Previous affiliations of Nathan S. Netanyahu include Universities Space Research Association & University of Maryland, College Park.
Papers
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Journal ArticleDOI
An efficient k-means clustering algorithm: analysis and implementation
Tapas Kanungo,David M. Mount,Nathan S. Netanyahu,Christine D. Piatko,Ruth Silverman,Angela Y. Wu +5 more
TL;DR: This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.
Journal ArticleDOI
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
TL;DR: In this paper, it was shown that given an integer k ≥ 1, (1 + ϵ)-approximation to the k nearest neighbors of q can be computed in additional O(kd log n) time.
Proceedings ArticleDOI
A local search approximation algorithm for k-means clustering
Tapas Kanungo,David M. Mount,Nathan S. Netanyahu,Christine D. Piatko,Ruth Silverman,Angela Y. Wu +5 more
TL;DR: This work considers the question of whether there exists a simple and practical approximation algorithm for k-means clustering, and presents a local improvement heuristic based on swapping centers in and out that yields a (9+ε)-approximation algorithm.
Proceedings ArticleDOI
An optimal algorithm for approximate nearest neighbor searching
TL;DR: It is shown that it is possible to preprocess a set of data points in real D-dimensional space in O(kd) time and in additional space, so that given a query point q, the closest point of S to S to q can be reported quickly.
Proceedings ArticleDOI
DeepSign: Deep learning for automatic malware signature generation and classification
TL;DR: The results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants, and the presented method achieves 98.6% classification accuracy using the signatures Generating malware signatures.