A robust dissimilarity-based neural network for temporal pattern recognition

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

6 被引用数 (Scopus)

抄録

Temporal pattern recognition is challenging because temporal patterns require extra considerations over other data types, such as order, structure, and temporal distortions. Recently, there has been a trend in using large data and deep learning, however, many of the tools cannot be directly used with temporal patterns. Convolutional Neural Networks (CNN) for instance are traditionally used for visual and image pattern recognition. This paper proposes a method using a neural network to classify isolated temporal patterns directly. The proposed method uses dynamic time warping (DTW) as a kernel-like function to learn dissimilarity-based feature maps as the basis of the network. We show that using the proposed DTW-NN, efficient classification of on-line handwritten digits is possible with accuracies comparable to state-of-the-art methods.

本文言語英語
ホスト出版物のタイトルProceedings - 2016 15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ページ265-270
ページ数6
ISBN(電子版)9781509009817
DOI
出版ステータス出版済み - 7 2 2016
イベント15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016 - Shenzhen, 中国
継続期間: 10 23 201610 26 2016

出版物シリーズ

名前Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
0
ISSN(印刷版)2167-6445
ISSN(電子版)2167-6453

その他

その他15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016
Country中国
CityShenzhen
Period10/23/1610/26/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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