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Domain Reduction Techniques for Sequential Fuzzy Indexing Tables – A Case Study

  • B. TusorEmail author
  • J. Bukor
  • L. Végh
  • O. Takáč
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)

Abstract

In recent years, single-board computers have been gaining popularity because they make it possible to use low cost, low energy consumption devices to solve complex tasks that would be much harder to solve with microcontrollers. However, these devices have much more limited capabilities both computation and memory-wise compared to traditional computers, which in turn limits the options available to use them for classification problems. One of the available options is using Lookup Table-based classifiers that require minimal computation, although in return they require more memory space. Sequential Fuzzy Indexing Tables are improved versions of Lookup Tables that require less memory, but for large problem spaces their storage cost is still very high. This is due to the size of its structure, which can be reduced with suitable domain conversion techniques. In this paper, multiple options are investigated, analyzed and compared in order to solve this problem.

Keywords

Data hashing Text processing Machine learning Domain reduction 

Notes

Acknowledgement

The publication was prepared with the financial support of Pallas Athéné Domus Educationis Foundation, project number: PADE-0117-5.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Mathematics and InformaticsJ. Selye UniversityKomárnoSlovakia

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