A Novel Naive Bayesian Approach to Inference with Applications to the MNIST Handwritten Digit Classification*

Research output: Contribution to book or proceedingConference articlepeer-review

1 Scopus citations

Abstract

Naive Bayesian approach is an effective method for many data analysis problems such as pattern classification and machine learning. However, it often suffers from the underflow problem when the input data has a high dimension. Such a problem is often addressed by taking logarithms and working in the transformed domain. In this paper we propose a novel approach to this problem based on geometric means and apply it to the classical MNIST handwritten digit classification problem. The results show that it not only achieves satisfactory accuracy but also demonstrates its power of presenting second best guesses that are meaningful and useful in the pattern classification domain.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1354-1358
Number of pages5
ISBN (Electronic)9781728176246
DOIs
StatePublished - Dec 2020
Event2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 - Las Vegas, United States
Duration: Dec 16 2020Dec 18 2020

Publication series

NameProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

Conference

Conference2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
Country/TerritoryUnited States
CityLas Vegas
Period12/16/2012/18/20

Scopus Subject Areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

Keywords

  • image classification
  • naive Bayesian analysis

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