Variance change point detection via artificial neural networks for data separation

Kyong Joo Oh, Myung Sang Moon, Tae Yoon Kim

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

In this article, it will be shown that a nonparametric and data-adaptive approach to the variance change point (VCP) detection problem is possible by formulating it as a pattern classification problem. Technical aspects of the VCP detector are discussed, which include its training strategy and selection of proper classification tool.

Original languageEnglish
Pages (from-to)239-250
Number of pages12
JournalNeurocomputing
Volume68
Issue number1-4
DOIs
Publication statusPublished - 2005 Oct 1

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Pattern recognition
Detectors
Neural networks

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

Oh, Kyong Joo ; Moon, Myung Sang ; Kim, Tae Yoon. / Variance change point detection via artificial neural networks for data separation. In: Neurocomputing. 2005 ; Vol. 68, No. 1-4. pp. 239-250.
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Variance change point detection via artificial neural networks for data separation. / Oh, Kyong Joo; Moon, Myung Sang; Kim, Tae Yoon.

In: Neurocomputing, Vol. 68, No. 1-4, 01.10.2005, p. 239-250.

Research output: Contribution to journalArticle

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