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http://jmlr.org/papers/volume11/forero10a/forero10a.pdf
CONSENSUS-BASED DISTRIBUTED SUPPORT VECTOR MACHINES to a classifier trained using the data of nodes that remain operational. But ev en if the net-work becomes disconnected, the proposed algorithm will stay operational with performance
https://www.researchgate.net/publication/3303738_Distributed_Support_Vector_Machines
A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is presented. Training data are assumed to come from the same distribution and are locally stored in a ...
https://arxiv.org/pdf/1811.11922
This paper studies distributed inference for linear support vector machine (SVM) for the binary classi cation task. Despite a vast literature on SVM, much less is known about the inferential properties of SVM, especially in a distributed setting. In this paper, we pro-pose a multi-round distributed linear-type (MDL) estimator for conductingCited by: 3
https://www.sciencedirect.com/science/article/pii/S089360801830039X
3. Distributed linear support vector machine in master–slave mode 3.1. Standard ADMM iterations. In Forero et al. (2010), a distributed SVM via ADMM in peer-to-peer mode was proposed. This section introduces the reformulation of the distributed SVM via ADMM in the master–slave mode and presents a …Cited by: 1
https://ieeexplore.ieee.org/document/4650875/
Distributed regression over sensor networks: An support vector machine approach Abstract: This paper presents a distributed support vector regression (SV R) algorithm for sensor networks. The idea behind this algorithm is to make use of the structure similarity between sensor networks and SV Rs with 2D input data in order to implement SV R in a ...
https://arxiv.org/abs/1811.11922
Nov 29, 2018 · The growing size of modern data brings many new challenges to existing statistical inference methodologies and theories, and calls for the development of distributed inferential approaches. This paper studies distributed inference for linear support vector machine (SVM) for the binary classification task. Despite a vast literature on SVM, much less is known about the inferential …Cited by: 3
https://ieeexplore.ieee.org/abstract/document/8526323
Abstract: Support Vector Machines (SVM) are widely used as supervised learning models to solve the classification problem in machine learning. Training SVMs for large datasets is an extremely challenging task due to excessive storage and computational requirements. To tackle so-called big data problems, one needs to design scalable distributed algorithms to parallelize the model training and ...Author: Jyotikrishna Dass, Vivek Sarin, Rabi N. Mahapatra
https://experts.umn.edu/en/publications/consensus-based-distributed-support-vector-machines
abstract = "This paper develops algorithms to train support vector machines when training data are distributed across different nodes, and their communication to a centralized processing unit is prohibited due to, for example, communication complexity, scalability, or privacy reasons.Cited by: 366
https://www.sciencedirect.com/science/article/pii/S002002551830567X
Recently, the research on semi-supervised support vector machine (S 3 VM) has received much attention, and many S 3 VM algorithms have been proposed. Existing studies have shown that S 3 VM is effective especially in the situations where labeled data is scarce. Nevertheless, most of existing S 3 VM algorithms belong to centralized learning, that is, all the data is stored and processed at a ...Cited by: 1
http://scholarworks.uno.edu/cgi/viewcontent.cgi?article=1711&context=td
Distributed Support Vector Machine Learning A Thesis Submitted to the Graduate Faculty of the University of New Orleans in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Bioinformatics by Kenneth C. Armond …
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