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https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf
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 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 ...Cited by: 3108
https://www.microsoft.com/en-us/research/publication/fast-training-of-support-vector-machines-using-sequential-minimal-optimization/
This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO. 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 …Cited by: 7758
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 very large quadratic ...Author: John C. Platt
https://www.researchgate.net/publication/2624239_Sequential_Minimal_Optimization_A_Fast_Algorithm_for_Training_Support_Vector_Machines
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 very large quadratic ...Author: John C. Platt
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.560
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): 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 very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems.
https://www.sciencedirect.com/science/article/pii/S0925231206001871
A parallel version of sequential minimal optimization (SMO) is developed in this paper for fast training support vector machine (SVM). Up to now, SMO is one popular algorithm for training SVM, but it still requires a large amount of computation time for solving large size problems.Cited by: 20
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://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf
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 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 ...Cited by: 3108
https://www.microsoft.com/en-us/research/publication/fast-training-of-support-vector-machines-using-sequential-minimal-optimization/
This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO. 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 …Cited by: 7758
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 very large quadratic ...Author: John C. Platt
https://www.researchgate.net/publication/2624239_Sequential_Minimal_Optimization_A_Fast_Algorithm_for_Training_Support_Vector_Machines
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 very large quadratic ...Author: John C. Platt
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.560
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): 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 very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems.
https://www.sciencedirect.com/science/article/pii/S0925231206001871
A parallel version of sequential minimal optimization (SMO) is developed in this paper for fast training support vector machine (SVM). Up to now, SMO is one popular algorithm for training SVM, but it still requires a large amount of computation time for solving large size problems.Cited by: 20
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=299094.299105
Xiaoyun Wu , Rohini Srihari, New ν-support vector machines and their sequential minimal optimization, Proceedings of the Twentieth International Conference on International Conference on Machine Learning, p.824-831, August 21-24, 2003, Washington, DC, USACited by: 7758
https://www.semanticscholar.org/paper/Sequential-Minimal-Optimization%3A-A-Fast-Algorithm-Platt/53fcc056f79e04daf11eb798a7238e93699665aa
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 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 ...
http://citeseer.ist.psu.edu/showciting?cid=170533
Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs) are particularly promising because they are very accurate, quick to train, and quick to evaluate. 1.1 Keywords Text categorization, classification, support vector machines, machine learning, information management.
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