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Teaching Machine Learning: A Geometric View of Naïve Bayes

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Research and Advanced Technology for Digital Libraries (TPDL 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9316))

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Abstract

In this demo, we present two applications which allow users to ‘see’ a geometric interpretation of the Bayes’ rule and interact with a Naïve Bayes text classifier on a real dataset, namely the Reuters-21578 newswire collection. The main objective of this demo is to show how the pattern recognition capabilities of the human increase the effectiveness of the classifier even when technical details are not known in advance or the user is not an expert in the field. These two applications were developed with the R package Shiny; they have been deployed online and they are freely accessible from the links indicated in the paper.

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Notes

  1. 1.

    http://www.daviddlewis.com/resources/testcollections/reuters21578/.

  2. 2.

    https://gmdn.shinyapps.io/bayes2d/.

  3. 3.

    https://gmdn.shinyapps.io/shinyK/.

References

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Author information

Authors and Affiliations

  1. Deptartment of Information Engineering, University of Padua, Padua, Italy

    Giorgio Maria Di Nunzio

Authors
  1. Giorgio Maria Di Nunzio
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Corresponding author

Correspondence to Giorgio Maria Di Nunzio .

Editor information

Editors and Affiliations

  1. Ionian University, Corfu, Greece

    Sarantos Kapidakis

  2. Poznań Supercomputing and Networking Center, Poznań, Poland

    Cezary Mazurek

  3. Networking Center, Poznań Supercomputing and, Poznań, Poland

    Marcin Werla

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© 2015 Springer International Publishing Switzerland

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Di Nunzio, G.M. (2015). Teaching Machine Learning: A Geometric View of Naïve Bayes. In: Kapidakis, S., Mazurek, C., Werla, M. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2015. Lecture Notes in Computer Science(), vol 9316. Springer, Cham. https://doi.org/10.1007/978-3-319-24592-8_31

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  • DOI: https://doi.org/10.1007/978-3-319-24592-8_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24591-1

  • Online ISBN: 978-3-319-24592-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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