Computational performance optimization of support vector machine based on support vectors

[eyouso]

Title

Computational performance optimization of support vector machine based on support vectors

URL

https://dl.acm.org/doi/10.1016/j.neucom.2016.04.059

Summary

Describes the improved performance of using only support vectors to train SVMs

Key Points

By using only support vectors in training, a lot of space that is typically wasted on irrelevant training data is eliminated.

Citation

@Article{10.1016/j.neucom.2016.04.059,
author = {Wang, Xuesong and Huang, Fei and Cheng, Yuhu},
title = {Computational performance optimization of support vector machine based on support vectors},
year = {2016},
issue_date = {October 2016},
publisher = {Elsevier Science Publishers B. V.},
address = {NLD},
volume = {211},
number = {C},
issn = {0925-2312},
url = {https://doi.org/10.1016/j.neucom.2016.04.059},
doi = {10.1016/j.neucom.2016.04.059},
abstract = {The computational performance of support vector machine (SVM) mainly depends on the size and dimension of training sample set. Because of the importance of support vectors in the determination of SVM classification hyperplane, a kind of method for computational performance optimization of SVM based on support vectors is proposed. On one hand, at the same time of the selection of super-parameters of SVM, according to Karush-Kuhn-Tucker condition and on the precondition of no loss of potential support vectors, we eliminate non-support vectors from training sample set to reduce sample size and thereby to reduce the computation complexity of SVM. On the other hand, we propose a simple intrinsic dimension estimation method for SVM training sample set by analyzing the correlation between number of support vectors and intrinsic dimension. Comparative experimental results indicate the proposed method can effectively improve computational performance.},
journal = {Neurocomput.},
month = {oct},
pages = {66–71},
numpages = {6},
keywords = {Support vector machine, Support vector, Sample size, Intrinsic dimension, Computational performance}
}

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