Analytical decision boundary feature extraction for neural networks with multiple hidden layers

Jinwook Go, Chulhee Lee

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

A feature extraction method based on decision boundaries has been proposed for neural networks. The method is based on the fact that normal vectors to the decision boundary provide the information necessary for discriminating between classes. However, it is observed that the previous implementation of numerical approximation of the gradient has resulted in some performance loss and a long processing time. In this paper, we propose a new method to calculate normal vectors analytically for neural networks with multiple hidden layers. Experiments showed noticeable improvements in performance and speed.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsNicolai Petkov, Michel A. Westenberg
PublisherSpringer Verlag
Pages579-587
Number of pages9
ISBN (Print)3540407308, 9783540407300
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2756
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Go, J., & Lee, C. (2003). Analytical decision boundary feature extraction for neural networks with multiple hidden layers. In N. Petkov, & M. A. Westenberg (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 579-587). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2756). Springer Verlag. https://doi.org/10.1007/978-3-540-45179-2_71