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https://www.microsoft.com/en-us/research/publication/fast-training-of-support-vector-machines-using-sequential-minimal-optimization/
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop.Cited by: 7758
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf
Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as anCited by: 3108
https://www.researchgate.net/publication/234786663_Fast_Training_of_Support_Vector_Machines_Using_Sequential_Minimal_Optimization
This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a …Author: John C. Platt
https://link.springer.com/article/10.1007/s11633-005-0006-4
Jul 01, 2005 · This paper presents a new algorithm for Support Vector Machine (SVM) training, which trains a machine based on the cluster centers of errors caused by the current machine. Experiments with various training sets show that the computation time of this new algorithm scales almost linear with training set size and thus may be applied to much larger training sets, in comparison to standard ...Cited by: 3
https://ieeexplore.ieee.org/document/4731075/
Nov 19, 2008 · Fast training Support Vector Machines using parallel sequential minimal optimization Abstract: One of the key factors that limit support vector machines (SVMs) application in large sample problems is that the large-scale quadratic programming (QP) that arises from SVMs training cannot be easily solved via standard QP technique.
https://dl.acm.org/citation.cfm?id=299105
Fast training of support vector machines using sequential minimal optimization. Pages 185–208. Previous Chapter Next Chapter. ABSTRACT. No abstract available. Index Terms. Fast training of support vector machines using sequential minimal optimization. Computing methodologies. Artificial …Cited by: 7758
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