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author:

Huang, Liqin (Huang, Liqin.) [1] | Lin, Liqun (Lin, Liqun.) [2] | Liu, Yanhuang (Liu, Yanhuang.) [3]

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EI

Abstract:

MapReduce framework of cloud computing has an effective way to achieve massive text categorization. In this paper a distributed parallel text training algorithm in cloud computing environment based on multi-class Support Vector Machines(SVM) is designed. In cloud computing environment Map tasks realize distributing various types of samples and Reduce tasks realize the specific SVM training. Experimental results show that the execution time of text training decreases with the number of Reduce tasks increasing. Also a parallel text classifying based on cloud computing is designed and implemented, which classify the unknown type texts. Experimental results show that the speed of text classifying increases with the number of Map tasks increasing. © (2013) Trans Tech Publications, Switzerland.

Keyword:

Cloud computing Communication Computer systems Support vector machines Text processing

Community:

  • [ 1 ] [Huang, Liqin]College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
  • [ 2 ] [Lin, Liqun]College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
  • [ 3 ] [Liu, Yanhuang]College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China

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Source :

ISSN: 1660-9336

Year: 2013

Volume: 311

Page: 158-163

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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