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https://www.csie.ntu.edu.tw/~cjlin/papers/generalSMO.pdf
A Study on SMO-type Decomposition Methods for Support Vector Machines Pai-Hsuen Chen, Rong-En Fan, and Chih-Jen Lin Department of Computer Science, National Taiwan University, Taipei 106, Taiwan [email protected] Abstract Decomposition methods are currently one of the major methods for training support vector ma-chines.
https://www.researchgate.net/publication/6928614_A_Study_on_SMO-Type_Decomposition_Methods_for_Support_Vector_Machines
If Q is positive definite and the nondegeneracy assumption holds, the SMO-type decomposition methods are shown to be linear convergent to the solution of (7) in [21]. It is well-known that the PG ...
https://www.semanticscholar.org/paper/A-study-on-SMO-type-decomposition-methods-for-Chen-Fan/57a18d3dbef4d79de48a2ffedfb7d5be2dfec5ac
Decomposition methods are currently one of the major methods for training support vector machines. They vary mainly according to different working set selections. Existing implementations and analysis usually consider some specific selection rules. This paper studies sequential minimal optimization type decomposition methods under a general and flexible way of choosing the two …
https://www.academia.edu/2834430/A_study_on_SMO-type_decomposition_methods_for_support_vector_machines
Abstract Decomposition methods are currently one of the major methods for training support vector machines. They vary mainly according to different working set selections. Existing implementations and analysis usually consider some specific selection
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.6586
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Decomposition methods are currently one of the major methods for training support vector machines. They vary mainly according to different working set selections.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.3911
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Decomposition methods are currently one of the major methods for training support vector ma-chines. They vary mainly according to different working set selections. Existing implementations and analysis usually consider some specific selection rules. In this article, we study Sequential Minimal Optimization (SMO)-type ...
https://www.researchgate.net/publication/221393885_Training_Support_Vector_Machines_via_SMO-Type_Decomposition_Methods
The decomposition method is currently one of the major methods for solving the convex quadratic optimization problems being associated with Support Vector Machines (SVM-optimization).
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3590457/
Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed.Cited by: 3
https://link.springer.com/chapter/10.1007/11564089_6
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3734) This article gives a comprehensive study on SMO-type (Sequential Minimal Optimization) decomposition methods for training support vector machines. We propose a general and flexible selection of the two-element working set.Cited by: 5
https://www.sciencedirect.com/science/article/pii/S0925231218309640
Decomposition methods play an important role in solving large-scale quadratic programming (QP) problems arising from support vector machines (SVMs). In this paper, we study convergence of general decomposition methods for SVMs. We prove that, under a mild condition on the working set selection, a decomposition algorithm stops within...Author: Qiaozhi Zhang, Di Wang, Yanguo Wang
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