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A STUDY ON THE ERROR OF DISTRIBUTED ALGORITHMS FOR BIG DATA CLASSIFICATION WITH SVM

Part of: Artificial intelligence (68Txx)

Published online by Cambridge University Press:  07 March 2017

CHENG WANG Open the ORCID record for CHENG WANG [Opens in a new window]  and
FEILONG CAO Open the ORCID record for FEILONG CAO [Opens in a new window]
CHENG WANG
Affiliation:
Applied Mathematics Department of China Jiliang University, China email [email protected], [email protected]
FEILONG CAO*
Affiliation:
Applied Mathematics Department of China Jiliang University, China email [email protected], [email protected]
*
✉[email protected]
Rights & Permissions [Opens in a new window]

Abstract

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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.

Keywords

distributed algorithmbig datasupport vector machineTsybakov exponentgeometric noise exponent

MSC classification

Primary: 68T05: Learning and adaptive systems
Type
Research Article
Information
The ANZIAM Journal , Volume 58 , Issue 3-4 , April 2017 , pp. 231 - 237
Copyright
© 2017 Australian Mathematical Society 

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