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https://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://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 ...
https://scikit-learn.org/stable/modules/svm.html
The support vector machines in scikit-learn support both dense (numpy.ndarray and convertible to that by numpy.asarray) and sparse (any scipy.sparse) sample vectors as input. However, to use an SVM to make predictions for sparse data, it must have been fit on such data.
https://cran.r-project.org/web/packages/parallelSVM/parallelSVM.pdf
Package ‘parallelSVM’ ... type Support-Vector-Machine can be used as a classification machine, as a regression machine, or for novelty detection. Depending of whether y is a factor or not, ... package in R 2.14.0, provides functions for parallel execution of R code on machines with multiple cores or processors, using the system fork call ...
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.
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
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.7569
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 loads only ...
https://research.google/pubs/pub34638/
We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of workCited by: 228
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