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      Voting base online sequential extreme learning machine for multi-class classification

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      Author
      Cao, Jiuwen
      Lin, Zhiping
      Huang, Guang-Bin
      Date of Issue
      2013
      Conference Name
      IEEE International Symposium on Circuits and Systems (2013 : Beijing, China)
      School
      School of Electrical and Electronic Engineering
      Abstract
      In this paper, we propose a voting based online sequential extreme learning machine (VOS-ELM) for single hidden layer feedforward networks (SLFNs) to perform the online sequential multi-class classification. Utilizing the recent voting based extreme learning machine (V-ELM) and the online sequential extreme learning machine (OS-ELM), the newly developed VOS-ELM is able to classify online sequences by learning data one-by-one or chunk-by-chunk with fixed or varying chunk size and to reach a higher classification accuracy than the original OS-ELM. Simulations on several real world classification datasets show that VOS-ELM outperforms OS-ELM as well as several state-of-the-art online sequential algorithms.
      Subject
      DRNTU::Engineering::Electrical and electronic engineering
      Type
      Conference Paper
      Collections
      • EEE Conference Papers
      http://dx.doi.org/10.1109/ISCAS.2013.6572344
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