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https://arxiv.org/pdf/1404.1066v1.pdf
plicitly parallel algorithm which is surprisingly efficient, permits a much simpler implementation, and leads to unprecedented speedups in SVM training. 1 Introduction Kernel support vector machines (SVM) are arguably among the most established machine learning algorithms.
https://www.researchgate.net/publication/261369013_Parallel_Support_Vector_Machines_in_Practice
In this paper, we evaluate the performance of various parallel optimization methods for Kernel Support Vector Machines on multicore CPUs and GPUs.
https://arxiv.org/abs/1404.1066
In this paper, we evaluate the performance of various parallel optimization methods for Kernel Support Vector Machines on multicore CPUs and GPUs. In particular, we provide the first comparison of algorithms with explicit and implicit parallelization. Most existing parallel implementations for multi-core or GPU architectures are based on explicit parallelization of Sequential Minimal ...Cited by: 14
https://journals.sagepub.com/doi/10.1260/1748-3018.4.2.231
Jun 01, 2010 · For the first time, a parallel implementation of support vector machines is used to accelerate the model training process. Our experimental results show very good performance of this approach, paving the way for further applications of support vector machines method on large energy consumption datasets.Cited by: 66
https://bibliographie.uni-tuebingen.de/xmlui/bitstream/handle/10900/49015/pdf/tech_21.pdf
Parallel Support Vector Machines Dominik Brugger Arbeitsbereich Technische Informatik Eberhard-Karls Universit¨at T ¨ubingen Sand 13, 72074 Tubingen¨ [email protected] Abstract The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and regression problems. SVMs have gained
https://papers.nips.cc/paper/2608-parallel-support-vector-machines-the-cascade-svm.pdf
Support Vector Machines [1] are powerful classification and regression tools, but their compute and storage requirements increase rapidly with the number of training vectors, putting many problems of practical interest out of their reach. The core of an SVM is a quadratic programming problem (QP), separating support vectors from the
https://en.wikipedia.org/wiki/Support-vector_machine
The support-vector clustering algorithm, created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, and is one of the most widely used clustering algorithms in industrial applications. [citation needed
https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html
Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression. In this section, we will develop the intuition behind support vector machines and their use in classification problems. We begin with the standard imports:
https://www.kdnuggets.com/2014/03/top-tweets-mar21-23.html
Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models, and Adaptive Boosting buff.ly/1jiSsoB Top 10 Tweets. Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models, and Adaptive Boosting buff.ly/1jiSsoB
http://papers.nips.cc/paper/3202-parallelizing-support-vector-machines-on-distributed-computers.pdf
Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to ...
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