A hyper-box approach using relational databases for large scale machine learning

Stelios E. Papadakis, Vangelis A. Stykas, George Mastorakis, Constandinos X. Mavromoustakis

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

2 Citations (Scopus)

Abstract

In this paper We follow a simple approach which allows the implementation of machine learning (ML for short) techniques to large data sets. More specifically, we study the case of on-demand dynamic creation of a local model in the neighborhood of a target datum instead of creating a global one on the whole training data set. This approach exploits the advanced data structures and algorithms, embedded in modern relational databases, to identify the neighborhood of a target datum, rapidly. Preliminary experimental results from a large scale classification problem (HIGGS dataset) show that the typical machine learning techniques are applicable to large data sets through this approach, under particular conditions. We highlight some restrictions of the method and some issues arising by implementing it.

Original languageEnglish
Title of host publication2014 International Conference on Telecommunications and Multimedia, TEMU 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages69-73
Number of pages5
ISBN (Electronic)9781479932009
DOIs
Publication statusPublished - 7 Oct 2014
Event2014 International Conference on Telecommunications and Multimedia, TEMU 2014 - Heraklion, Greece
Duration: 28 Jul 201430 Jul 2014

Other

Other2014 International Conference on Telecommunications and Multimedia, TEMU 2014
CountryGreece
CityHeraklion
Period28/07/1430/07/14

Keywords

  • Big Data
  • Higgs
  • Hyper-box
  • Machine Learning
  • Support Vector Machines

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  • Cite this

    Papadakis, S. E., Stykas, V. A., Mastorakis, G., & Mavromoustakis, C. X. (2014). A hyper-box approach using relational databases for large scale machine learning. In 2014 International Conference on Telecommunications and Multimedia, TEMU 2014 (pp. 69-73). [6917738] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TEMU.2014.6917738