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http://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/221619261_Parallel_Support_Vector_Machines_The_Cascade_SVM
Parallel Support Vector Machines: The Cascade SVM. ... We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with ...
http://citeseer.ist.psu.edu/viewdoc/citations?doi=10.1.1.140.7614
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing the whole training set in one optimization step, the data are split into subsets and optimized separately with multiple ...
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.7614
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing the whole training set in one optimization step, the data are split into subsets and optimized separately with multiple ...
https://www.hindawi.com/journals/acisc/2015/216132/
Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results ...Cited by: 1
https://www.semanticscholar.org/paper/Parallel-Support-Vector-Machines%3A-The-Cascade-SVM-Graf-Cosatto/61abf3eb0f653b67c0eb42c527b6620db51d4f3f
We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing the whole training set in one optimization step, the data are split into subsets and optimized separately with multiple SVMs. The partial results are combined and filtered again in a 'Cascade' of ...
https://www.statistik.tu-dortmund.de/~bischl/mypapers/support_vector_machines_on_large_data_sets_simple_parallel_approaches.pdf
Support Vector Machines on Large Data Sets: Simple Parallel Approaches 5 5 Stepwise Bagging It can easily be seen that the possibility to parallelize the Cascade SVM decreases in every step. This leads to the problem that an increasing number of cores will stay idle during the later stages, and in the last stage only one core can be used.Cited by: 9
https://www2.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-11.pdf
been done to accelerate SVM training, it is still very significant for larger training sets. In this paper, we show how Support Vector Machine training and classification can be adapted to a highly parallel, yet widely available and affordable computing platform: the graphics processor, or more specifically,Cited by: 460
https://www.semanticscholar.org/paper/Support-Vector-Machines-on-Large-Data-Sets%3A-Simple-Meyer-Bischl/b18c815859457e50a793b9f778d2dcc842cb0cc5
Support Vector Machines (SVMs) are well-known for their excellent performance in the field of statistical classification. Still, the high computational cost due to the cubic runtime complexity is problematic for larger data sets. To mitigate this, Graf et al. (Adv. Neural Inf. Process. Syst. 17:521–528, 2005) proposed the Cascade SVM. It is a simple, stepwise procedure, in which the SVM is ...
https://cran.r-project.org/web/packages/parallelSVM/parallelSVM.pdf
2 parallelSVM-package parallelSVM-package Parallel-voting version of Support-Vector-Machine Description By sampling your data, running the Support-Vector-Machine algorithm on these samples in par-allel on your own machine and letting your models vote on a prediction, we return much faster
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