Variance change point detection via artificial neural networks for data separation

Kyong Joo Oh, Myung Sang Moon, Tae Yoon Kim

Research output: Contribution to journalArticlepeer-review

8 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

Bibliographical note

Funding Information:
This work was supported by the Korea Research Foundation Grant funded by Korea Government (KRF 2003-C0008).

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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