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https://www.researchgate.net/profile/Dominik_Brugger/publication/251704511_Parallel_Support_Vector_Machines/links/02e7e5374f9676472e000000.pdf
Parallel Support Vector Machines Dominik Brugger WSI-2006-01 ISSN 0946-3851 Dominik Brugger Arbeitsbereich Technische Informatik Sand 13, 72074 Tubingen¨ [email protected] c …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.333.3078
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and regression problems. SVMs have gained widespread use in recent years because of successful applications like character recognition and the profound theoretical underpinnings concerning generalization performance.
https://core.ac.uk/display/22977423
By Dominik Brugger, Dominik Brugger, Arbeitsbereich Technische Informatik, Dominik Brugger, Arbeitsbereich Technische Informatik and Eberhard-karls Universität Tübingen Abstract The Support Vector Machine (SVM) is a supervised algorithm for the …
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://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://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/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://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
https://www.sciencedirect.com/science/article/pii/S0950705119300450
In contrast to other non-parallel hyperplane Universum support vector machines, the proposed U-NHSVM simultaneously generates two non-parallel hyperplanes, such that each plane approaches the corresponding class and diverges from the other. The margin between the two classes is directly maximized by solving one single quadratic programming problem.Cited by: 9
https://towardsdatascience.com/support-vector-machines-svm-c9ef22815589
Oct 20, 2018 · Support vector machines so called as SVM is a supervised learning algorithm which can be used for classification and regression problems as support vector classification (SVC) and support vector regression (SVR). It is used for smaller dataset as it takes too long to process.
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