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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
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 ...
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 intelligence.Cited by: 7758
https://dl.acm.org/citation.cfm?id=299094.299105
Javier Acevedo , Saturnino Maldonado , Sergio Lafuente , Hilario Gomez , Pedro Gil, Model selection for support vector machines using ant colony optimization in an electronic nose application, Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence, September 04-07, 2006, Brussels, BelgiumCited by: 7758
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 · 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. The sequential minimal optimization (SMO) is current one of the major methods for solving SVMs. This method, to a certain extent, can decrease the ...
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/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 …Author: John C. Platt
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 …Author: John C. Platt
https://dl.acm.org/citation.cfm?id=299094.299105
Javier Acevedo , Saturnino Maldonado , Sergio Lafuente , Hilario Gomez , Pedro Gil, Model selection for support vector machines using ant colony optimization in an electronic nose application, Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence, September 04-07, 2006, Brussels, BelgiumCited by: 7758
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. …Cited by: 7758
https://en.wikipedia.org/wiki/Sequential_minimal_optimization
Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). It was invented by John Platt in 1998 at Microsoft Research. SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.Class: Optimization algorithm for training support vector …
http://cs229.stanford.edu/materials/smo.pdf
CS 229, Autumn 2009 The Simplified SMO Algorithm 1 Overview of SMO This document describes a simplified version of the Sequential Minimal Optimization (SMO) algorithm for training support vector machines that you will implement for problem set #2. The full algorithm is described in John Platt’s paper1 [1], and much of this document is based
http://citeseer.ist.psu.edu/showciting?cid=170533
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) problem. This paper proposes an algorithm for training SVMs: Sequential Minimal Optimization,or SMO. SMO breaks the large QP problem into a series of smallest possible QP problems which are analytically solvable.
http://citeseer.ist.psu.edu/showciting?cid=177404
Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner.
https://en.wikipedia.org/wiki/Sequential_minimal_optimization
Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). It was invented by John Platt in 1998 at Microsoft Research. SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.
https://www.scientific.net/AMM.29-32.947
A fast training support vector machine using parallel sequential minimal optimization is presented in this paper. Up to now, sequential minimal optimization (SMO) is one of the major algorithms for training SVM, but it still requires a large amount of computation time for the large sample problems.
https://github.com/mazefeng/svm
Tools that implement of the classic SMO (Sequential Minimal Optimization) algorithm for traning SVMs - mazefeng/svm. Tools that implement of the classic SMO (Sequential Minimal Optimization) algorithm for traning SVMs - mazefeng/svm ... "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines." (1998).
https://link.springer.com/chapter/10.1007/0-387-37452-3_7
Fast training of support vector machines using sequential minimal optimisation. In B. Scholkopf, C. Burges and A. Smola, editors, Advances in Kernel Methods - support vector learning, pages 185–208. MIT press, Cambridge, MA, 1999 Google Scholar
http://crsouza.com/2010/04/27/kernel-support-vector-machines-for-classification-and-regression-in-c/
Apr 27, 2010 · Kernel methods in general have gained increased attention in recent years, partly due to the grown of popularity of the Support Vector Machines. Support Vector Machines are linear classifiers and regressors that, through the Kernel trick, operate in reproducing Kernel Hilbert spaces and are thus able to perform non-linear classification and ...
https://www.coursehero.com/file/17168256/smo-book/
Generic author design sample pages 2000/08/14 13:12 46 Fast Training of Support Vector Machines using Sequential Minimal Optimization 12.2.1 Constraints on 2 Solving for Two Lagrange Multipliers In order to solve for the two Lagrange multipliers, SMO rst computes the constraints on these multipliers and then solves for the constrained maximum.
https://www.sciencedirect.com/science/article/pii/S0925231211002773
J.C. PlattFast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, USA (1999), pp. 185-208 ... N. Sundaram, K. KeutzerFast support vector machine training and classification on graphics processors. ICML ’08: Proceedings of the 25th ...
https://link.springer.com/content/pdf/10.1007%252F978-3-642-16687-7_64.pdf
A Sequential Minimal Optimization Algorithm for the All-Distances Support Vector Machine Diego Candel 1,RicardoNanculef˜ 1, Carlos Concha ,andH´ector Allende,2 1 Universidad T´ecnica Federico Santa Mar´ıa, Departamento de Inform´atica, CP 110-V Valpara´ıso, Chile
https://stackoverflow.com/questions/1757224/implementing-a-linear-binary-svm-support-vector-machine
Some pseudocode for the Sequential Minimal Optimization (SMO) method can be found in this paper by John C. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. There is also a Java implementation of the SMO algorithm, which is developed for research and educational purpose .
http://weka.sourceforge.net/doc.dev/weka/classifiers/functions/SMO.html
In the multi-class case, the predicted probabilities are coupled using Hastie and Tibshirani's pairwise coupling method. Note: for improved speed normalization should be turned off when operating on SparseInstances. For more information on the SMO algorithm, see J. Platt: Fast Training of Support Vector Machines using Sequential Minimal ...
https://deepai.org/publication/fast-multilevel-support-vector-machines
10/13/14 - Solving different types of optimization models (including parameters fitting) for support vector machines on large-scale training ...
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