Some Improvements to a Parallel Decomposition Technique for Training Support Vector Machines

Serafini, Thomas; Zanni, Luca; Zanghirati, Gaetano

doi:10.1007/11557265_7

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…In [49], the kernel matrix of SVM is approximated by block diagonal matrices so that the original optimization problem can be decomposed into hundreds of sub problems, which are easy to solve in a parallel fashion. Other parallel implementations can be found in [50][51][52][53][54].…”

Section: Review On Methods For Training Support Vector Machinesmentioning

confidence: 99%

Data selection based on decision tree for SVM classification on large data sets

Cervantes¹,

García-Lamont²,

López-Chau³

et al. 2015

Applied Soft Computing

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Please cite this article in press as: J. Cervantes, et al., Data selection based on decision tree for SVM classification on large data sets, Appl. Soft Comput. J. (2015), http://dx.a b s t r a c t Support Vector Machine (SVM) has important properties such as a strong mathematical background and a better generalization capability with respect to other classification methods. On the other hand, the major drawback of SVM occurs in its training phase, which is computationally expensive and highly dependent on the size of input data set. In this study, a new algorithm to speed up the training time of SVM is presented; this method selects a small and representative amount of data from data sets to improve training time of SVM. The novel method uses an induction tree to reduce the training data set for SVM, producing a very fast and high-accuracy algorithm. According to the results, the proposed algorithm produces results with similar accuracy and in a faster way than the current SVM implementations.

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Section: Review On Methods For Training Support Vector Machinesmentioning

confidence: 99%

Data selection based on decision tree for SVM classification on large data sets

Cervantes¹,

García-Lamont²,

López-Chau³

et al. 2015

Applied Soft Computing

View full text Add to dashboard Cite

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Distributed independent vector machine for big data classification problems

Almaspoor,

Safaei,

Salajegheh

et al. 2023

J Supercomput

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A Novel Method for Training Large Scale E-Business SVM Models in a Grid Computing Environment

Qin

2010

2010 International Conference on E-Business and E-Government

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The Support Vector Machines (SVM) become popular E-Business data mining tools recently, and the datasets of EBusiness are usually large-scale. If Support Vector Machines are trained on large-scale datasets, the training time will be very long and the classifier's accuracy will become lower too. As training a large-scale SVM is equated to solve a large-scale quadratic programming (QP) problem, so Path Following Interior Point Method (IPM) that can efficiently solve large scale QP problem in polynomial time is proposed to construct a new SVM learning algorithm on large-scale datasets. To improve the SVM learning efficiency, the dimensions of IPM direction equations are degraded first, then LDL T parallel decomposition method is used to solve the direction sub-equations efficiently, and the parallel algorithm is implemented in the ProActive grid-computing environment. The experiment results show that the new parallel SVM training algorithm is efficient and the SVM classifying accuracy is higher than libsvm.

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Some Improvements to a Parallel Decomposition Technique for Training Support Vector Machines

Cited by 3 publications

References 8 publications

Data selection based on decision tree for SVM classification on large data sets

Data selection based on decision tree for SVM classification on large data sets

Distributed independent vector machine for big data classification problems

A Novel Method for Training Large Scale E-Business SVM Models in a Grid Computing Environment

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