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https://link.springer.com/chapter/10.1007/978-0-387-09707-7_7
Support vector machines are a powerful machine learning technology, but the training process involves a dense quadratic optimization problem and is computationally expensive. We show how the problem can be reformulated to become suitable for high-performance parallel computing.Cited by: 5
https://www.researchgate.net/publication/226603678_High-Performance_Parallel_Support_Vector_Machine_Training
Support vector machines are a powerful machine learning technology, but the training process involves a dense quadratic optimization problem and is computationally expensive.
https://www2.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-11.pdf
Keywords: Support Vector Machines, Sequential Minimal Optimization, Graphics Processing Units Abstract Recent developments in programmable, highly parallel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learning algo-rithms. We describe a solver for Support Vector Machine training, using Platt’sCited by: 460
http://www.keerthis.com/parallel_SMO_IEEE.pdf
Institute of High Performance Computing, 1 Science Park Road, #01-01 the Capricorn, Science Park II, 117528 Singapore . Abstract ⎯ Sequential minimal optimization (SMO) is one popular algorithm for training support vector machine (SVM), but it still requires a large amount of computation time for solving large size problems.
https://dl.acm.org/ft_gateway.cfm?id=3280989&ftid=2037641&dwn=1
The immense amount of data created by digitalization requires parallel computing for machine-learning methods. While there are many parallel implementations for support vector machines (SVMs), there is no clear suggestion for every application scenario.Author: Shirin Tavara
https://dl.acm.org/doi/10.1145/1390156.1390170
Fast support vector machine training and classification on graphics processors ... highly parallel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learning algorithms. ... E., Bottou, L., Dourdanovic, I., & Vapnik, V. (2005). Parallel support vector machines: The cascade svm. In L. K. Saul, Y. Weiss ...
https://www2.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-11.pdf
Keywords: Support Vector Machines, Sequential Minimal Optimization, Graphics Processing Units Abstract Recent developments in programmable, highly parallel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learning algo-rithms. We describe a solver for Support Vector Machine training, using Platt’sCited by: 462
https://www.researchgate.net/publication/226603678_High-Performance_Parallel_Support_Vector_Machine_Training
Support vector machines are a powerful machine learning technology, but the training process involves a dense quadratic optimization problem and is computationally expensive.
https://www.researchgate.net/publication/221345076_Fast_support_vector_machine_training_and_classification_on_graphics_processors
Fast support vector machine training and classification on graphics processors ... have enabled high performance implementations of machine learning algo- rithms. We describe a solver for Support ...
http://icml2008.cs.helsinki.fi/papers/491.pdf
highly parallel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learning algo-rithms. We describe a solver for Support Vector Machine training running on a GPU, using the Sequential Minimal Optimization algorithm and an adaptive rst and second order working set selection heuristic, which
https://papers.nips.cc/paper/2608-parallel-support-vector-machines-the-cascade-svm.pdf
Parallel Support Vector Machines: The Cascade SVM Hans Peter Graf, Eric Cosatto, ... in particular to high-dimensional problems, remains to be seen. A parallelization scheme was proposed where the kernel matrix is approximated by a ... Training data, SVi: Support vectors produced by
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://www.researchgate.net/publication/226603678_High-Performance_Parallel_Support_Vector_Machine_Training
Support vector machines are a powerful machine learning technology, but the training process involves a dense quadratic optimization problem and is computationally expensive.
https://www.researchgate.net/publication/221345076_Fast_support_vector_machine_training_and_classification_on_graphics_processors
Fast support vector machine training and classification on graphics processors ... have enabled high performance implementations of machine learning algo- rithms. We describe a solver for Support ...
https://papers.nips.cc/paper/2608-parallel-support-vector-machines-the-cascade-svm.pdf
Parallel Support Vector Machines: The Cascade SVM Hans Peter Graf, Eric Cosatto, ... in particular to high-dimensional problems, remains to be seen. A parallelization scheme was proposed where the kernel matrix is approximated by a ... Training data, SVi: Support vectors produced by
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.
http://www.keerthis.com/parallel_SMO_IEEE.pdf
Institute of High Performance Computing, 1 Science Park Road, #01-01 the Capricorn, Science Park II, 117528 Singapore . Abstract ⎯ Sequential minimal optimization (SMO) is one popular algorithm for training support vector machine (SVM), but it still requires a large amount of computation time for solving large size problems.
http://icml2008.cs.helsinki.fi/papers/491.pdf
Fast Support Vector Machine Training and Classi cation on Graphics Processors ... (GPUs) have enabled high performance implementations of machine learning algo-rithms. We describe a solver for Support Vector Machine training running on a GPU, using the Sequential Minimal Optimization
https://buy.hpe.com/us/en/software/high-performance-computing-ai-software/c/c001007
High Performance Computing and AI Software. Back to Shop ... Do you need a development toolset that speeds code execution for high performance computing (HPC)? Intel Parallel Studio XE is a development suite that boosts application performance by taking advantage of the ever-increasing processor core count and vector register width available in ...
https://en.wikipedia.org/wiki/Support_vector_machine
Support-vector machine weights have also been used to interpret SVM models in the past. Posthoc interpretation of support-vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences. History
https://www2.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-11.pdf
Keywords: Support Vector Machines, Sequential Minimal Optimization, Graphics Processing Units Abstract Recent developments in programmable, highly parallel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learning algo-rithms. We describe a solver for Support Vector Machine training, using Platt’sCited by: 462
https://dl.acm.org/doi/10.1145/1390156.1390170
Fast support vector machine training and classification on graphics processors. ... highly parallel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learning algorithms. ... T., & Zanghirati, G. (2006). Parallel software for training large scale support vector machines on multiprocessor systems. J ...
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