Robust variable length data classification with extended sequential fuzzy indexing tables

A. Várkonyi-Kóczy, Balázs Tusor, J. T. Tóth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Recurrent Neural Networks are widely used tools for the classification of variable length data. However, their training is generally a very time-consuming task, especially for problems with high dimensions. The classification method proposed in this paper aims to provide a fast and simple alternative. Extended Sequential Fuzzy Indexing Tables are following the principle behind lookup table classifiers in that they realize an input-output association by mapping the problem space using arrays. The proposed network achieves this by breaking the multi-dimensional problem space down to a sequence of combinations, resulting in a flexible architecture that can work well with varying length data.

Original languageEnglish
Title of host publicationI2MTC 2017 - 2017 IEEE International Instrumentation and Measurement Technology Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509035960
DOIs
Publication statusPublished - Jul 5 2017
Event2017 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2017 - Torino, Italy
Duration: May 22 2017May 25 2017

Other

Other2017 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2017
CountryItaly
CityTorino
Period5/22/175/25/17

Keywords

  • Classification
  • Fuzzy logic
  • Indexing tables
  • Machine learning
  • Neural networks
  • Recurrent neural networks
  • Signal processing
  • Variable length data

ASJC Scopus subject areas

  • Instrumentation
  • Signal Processing
  • Biomedical Engineering

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  • Cite this

    Várkonyi-Kóczy, A., Tusor, B., & Tóth, J. T. (2017). Robust variable length data classification with extended sequential fuzzy indexing tables. In I2MTC 2017 - 2017 IEEE International Instrumentation and Measurement Technology Conference, Proceedings [7969971] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/I2MTC.2017.7969971