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Fix-Budget and Recurrent Data Mining for Online Haptic Perception

  • Lele Cao
  • Fuchun Sun
  • Xiaolong Liu
  • Wenbing Huang
  • Weihao Cheng
  • Ramamohanarao Kotagiri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

Haptic perception is to identify different targets from haptic input. Haptic data have two prominent features: sequentially real-time and temporally correlated, which calls for a fixed-budget and recurrent perception procedure. Based on an efficient-robust spatio-temporal feature representation, we handle the problem with a bounded online-sequential learning framework (MBS-ESN), and incorporates the strength of batch-regularization bootstrapping, bounded recursive reservoir, and momentum-based estimation. Experimental evaluations show that it outperforms the state-of-the-art methods by a large margin on test accuracy; and its training performance is superior to most compared models from aspects of computational complexity and storage efficiency.

Keywords

Haptic perception Echo state network Online learning Recurrent neural network Fixed-budget learning 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China with grant number 041320190.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lele Cao
    • 1
    • 2
  • Fuchun Sun
    • 1
  • Xiaolong Liu
    • 1
  • Wenbing Huang
    • 1
  • Weihao Cheng
    • 2
  • Ramamohanarao Kotagiri
    • 2
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Department of Computing and Information SystemsThe University of MelbourneMelbourneAustralia

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