Variance change point detection via artificial neural networks for data separation

Kyong Joo Oh; Myung Sang Moon; Tae Yoon Kim

doi:10.1016/j.neucom.2005.05.005

Variance change point detection via artificial neural networks for data separation

Kyong Joo Oh, Myung Sang Moon, Tae Yoon Kim

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	239-250
Number of pages	12
Journal	Neurocomputing
Volume	68
Issue number	1-4
DOIs	https://doi.org/10.1016/j.neucom.2005.05.005
Publication status	Published - 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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10.1016/j.neucom.2005.05.005

Cite this

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title = "Variance change point detection via artificial neural networks for data separation",

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.",

author = "Oh, {Kyong Joo} and Moon, {Myung Sang} and Kim, {Tae Yoon}",

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

year = "2005",

month = oct,

doi = "10.1016/j.neucom.2005.05.005",

language = "English",

volume = "68",

pages = "239--250",

journal = "Review of Economic Dynamics",

issn = "1094-2025",

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AU - Oh, Kyong Joo

AU - Moon, Myung Sang

AU - Kim, Tae Yoon

N1 - Funding Information: This work was supported by the Korea Research Foundation Grant funded by Korea Government (KRF 2003-C0008).

PY - 2005/10

Y1 - 2005/10

N2 - 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.

AB - 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.

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M3 - Article

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JO - Review of Economic Dynamics

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Variance change point detection via artificial neural networks for data separation

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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