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http://papers.nips.cc/paper/3202-parallelizing-support-vector-machines-on-distributed-computers.pdf
PSVM: Parallelizing Support Vector Machines on Distributed Computers Edward Y. Chang⁄, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, & Hang Cui Google Research, Beijing, China Abstract Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability,
https://bura.brunel.ac.uk/bitstream/2438/5452/1/FulltextThesis.pdf
Nasullah Khalid Alham (2011) Parallelizing Support Vector Machines for Scalable Image Annotation ii Abstract Machine learning techniques have facilitated image retrieval by automatically classifying and
https://link.springer.com/chapter/10.1007/978-3-642-20429-6_10
Aug 26, 2011 · Abstract. 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 perform …Cited by: 228
https://www.researchgate.net/publication/221620344_PSVM_Parallelizing_Support_Vector_Machines_on_Distributed_Computers
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 ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.473.6607
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): 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 …
https://github.com/openbigdatagroup/psvm
Mar 03, 2016 · If you wish to publish any work based on psvm, please cite our paper as: Edward Chang, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, and Hang Cui, PSVM: Parallelizing Support Vector Machines on Distributed Computers.
http://core.ac.uk/display/337841
Parallelizing support vector machines for scalable image annotation . ... (2007). PSVM: parallelizing support vector machines on distributed computers‖, ... (2006). Support vector machines ensemble based on fuzzy integral for classification‖, ISNN,
https://scikit-learn.org/stable/modules/svm.html
Support Vector Machine algorithms are not scale invariant, so it is highly recommended to scale your data. For example, scale each attribute on the input vector X to [0,1] or [-1,+1], or standardize it to have mean 0 and variance 1. Note that the same scaling must be applied to
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.
http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/svm.html
H2O’s implementation of support vector machine follows the PSVM:Parallelizing Support Vector Machineson Distributed Computers specification and can be …
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