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https://research.google/pubs/pub36261/
PSVM: Parallel Support Vector Machines with Incomplete Cholesky Factorization. Edward Y. Chang; Hongjie Bai; Kaihua Zhu; Hao Wang; Jian Li; Zhihuan Qiu; Scaling Up Machine Learning, Cambridge University Press (2010) Google Scholar Copy Bibtex Abstract. No abstract available; check out the Download or Google Scholar links above for publications ...Cited by: 7
https://storage.googleapis.com/pub-tools-public-publication-data/pdf/34638.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…
http://ai.deepq.com/psvm/
Why PSVM. Although widely used, Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. PSVM achieves memory reduction and computation speedup via a row-based parallel Incomplete Cholesky Factorization (ICF) algorithm and parallel Interior-Point Method(IPM).
http://worldcomp-proceedings.com/proc/p2015/PDP3367.pdf
Incomplete Cholesky Factorization (ICF) to approximate the original matrix Q nn to a smaller matrix as H np, where p˝n. This approach can improve the time efficiency of the computations. In this paper, we have chosen the PSVM software that solves the SVM problem by utilizing IPM solver. A. Parallel Support Vector Machines
https://bmi.cchmc.org/resources/software/google-psvm
psvm is a parallel implementation of a support vector machine. This software uses MPI to provide a fully parallel SVM, including nonlinear kernels etc. Parallelization is accomplished by an incomplete Cholesky factorization of the kernel matrix.
http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/svm.html
H2O’s implementation of support vector machine follows the PSVM: ... using row-based Incomplete Cholesky Factorization (\(\mathbf{Q} \approx \mathbf{H}\mathbf{H} ... matrix is used in the IPM algorithm in order to speed up the bottleneck of the Newton step and leverage the parallel …
https://www.researchgate.net/publication/221620344_PSVM_Parallelizing_Support_Vector_Machines_on_Distributed_Computers
PSVM [3]: a parallel kernel SVM solver by in-complete Cholesky factorization and a parallel interior point method. We test the performance of PSVM with the rank suggested by the original paper (n ...
http://www.seas.ucla.edu/~vandenbe/publications/svmcmpl.pdf
Support vector machine training using matrix completion ... discuss an incomplete Cholesky factorization algorithm for computing a low-rank approximation of a kernel matrix. The method requires calculating (but not storing) all the entries in the kernel ... The matrix completion problem has an interesting statistical interpretation. Suppose f is a
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 parallel …Cited by: 228
https://www.researchgate.net/publication/285136894_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 ...
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