Application of classification restricted boltzmann machine to medical domains

Jakub M. Tomczak*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Recent developments have demonstrated deep models to be very powerful generative models which are able to extract features automatically and obtain high predictive performance. Typically, a building block of a deep architecture is Restricted Boltzmann Machine (RBM). In this work, we focus on a variant of RBM adopted to the classification setting, which is known as Classification Restricted Boltzmann Machine. We claim that this model should be used as a stand-alone non-linear classifier which could be extremely useful in medical domains. Additionally, we show how to obtain sparse representation in RBM by adding a regularization term to the learning objective which enforces sparse solution. The considered classifier is then applied to five different medical domains.

Original languageEnglish
Pages (from-to)69-75
Number of pages7
JournalWorld Applied Sciences Journal
Volume31
Issue number14
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Classification
  • Diabetes
  • Medical domain
  • Oncology
  • Restricted boltzmann machine
  • Sparse

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