A study on the error of distributed algorithms for big data classification with SVM

Cheng Wang, Feilong Cao


The error of a distributed algorithm for big data classification with a support vector machine (SVM) is analysed in this paper. First, the given big data sets are divided into small subsets, on which the classical SVM with Gaussian kernels is used. Then, the classification error of the SVM for each subset is analysed based on the Tsybakov exponent, geometric noise, and width of the Gaussian kernels. Finally, the whole error of the distributed algorithm is estimated in terms of the error of each subset.



distributed algorithm, big data, support vector machine, Tsybakov exponent, geometric noise exponent

DOI: http://dx.doi.org/10.21914/anziamj.v58i0.11083

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ANZIAM Journal, ISSN 1446-8735, copyright Australian Mathematical Society.