Dynamic Latent Classification Model: Towards a More Expressive Model for Dynamic Classification

Shengtong Zhong, Ana M. Martínez, Thomas Dyhre Nielsen, Helge Langseth

Publikation: Konferencebidrag uden forlag/tidsskriftPaper uden forlag/tidsskriftForskningpeer review

Resumé

Monitoring a complex process often involves keeping an eye on hundreds or thousands of sensors to deter- mine whether or not the process is under control. We have been working with dynamic data from an oil pro- duction facility in the North sea, where unstable situa- tions should be identified as soon as possible. Motivated by this problem setting, we propose a generative model for dynamic classification in continuous domains. At each time point the model can be seen as combining a naive Bayes model with a mixture of factor analyzers (FA). The latent variables of the FA are used to capture the dynamics in the process as well as modeling dependences between attributes.
OriginalsprogEngelsk
Publikationsdato2010
StatusUdgivet - 2010
BegivenhedThe Florida Artificial Intelligence Research Society Conference - Daytona Beach, Florida, USA
Varighed: 19 maj 2010 → …
Konferencens nummer: 23

Konference

KonferenceThe Florida Artificial Intelligence Research Society Conference
Nummer23
LandUSA
ByDaytona Beach, Florida
Periode19/05/2010 → …

Fingerprint

Monitoring
Sensors
Oils

Citer dette

Zhong, S., Martínez, A. M., Nielsen, T. D., & Langseth, H. (2010). Dynamic Latent Classification Model: Towards a More Expressive Model for Dynamic Classification. Afhandling præsenteret på The Florida Artificial Intelligence Research Society Conference, Daytona Beach, Florida, USA.
Zhong, Shengtong ; Martínez, Ana M. ; Nielsen, Thomas Dyhre ; Langseth, Helge. / Dynamic Latent Classification Model : Towards a More Expressive Model for Dynamic Classification. Afhandling præsenteret på The Florida Artificial Intelligence Research Society Conference, Daytona Beach, Florida, USA.
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title = "Dynamic Latent Classification Model: Towards a More Expressive Model for Dynamic Classification",
abstract = "Monitoring a complex process often involves keeping an eye on hundreds or thousands of sensors to determine whether or not the process is under control. We have been working with dynamic data from an oil production facility in the North sea, where unstable situations should be identified as soon as possible. Motivated by this problem setting, we propose a generative model for dynamic classification in continuous domains. At each time point the model can be seen as combining a naive Bayes model with a mixture of factor analyzers (FA). The latent variables of the FA are used to capture the dynamics in the process as well as modeling dependences between attributes.",
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Zhong, S, Martínez, AM, Nielsen, TD & Langseth, H 2010, 'Dynamic Latent Classification Model: Towards a More Expressive Model for Dynamic Classification' Paper fremlagt ved The Florida Artificial Intelligence Research Society Conference, Daytona Beach, Florida, USA, 19/05/2010, .

Dynamic Latent Classification Model : Towards a More Expressive Model for Dynamic Classification. / Zhong, Shengtong; Martínez, Ana M.; Nielsen, Thomas Dyhre; Langseth, Helge.

2010. Afhandling præsenteret på The Florida Artificial Intelligence Research Society Conference, Daytona Beach, Florida, USA.

Publikation: Konferencebidrag uden forlag/tidsskriftPaper uden forlag/tidsskriftForskningpeer review

TY - CONF

T1 - Dynamic Latent Classification Model

T2 - Towards a More Expressive Model for Dynamic Classification

AU - Zhong, Shengtong

AU - Martínez, Ana M.

AU - Nielsen, Thomas Dyhre

AU - Langseth, Helge

PY - 2010

Y1 - 2010

N2 - Monitoring a complex process often involves keeping an eye on hundreds or thousands of sensors to determine whether or not the process is under control. We have been working with dynamic data from an oil production facility in the North sea, where unstable situations should be identified as soon as possible. Motivated by this problem setting, we propose a generative model for dynamic classification in continuous domains. At each time point the model can be seen as combining a naive Bayes model with a mixture of factor analyzers (FA). The latent variables of the FA are used to capture the dynamics in the process as well as modeling dependences between attributes.

AB - Monitoring a complex process often involves keeping an eye on hundreds or thousands of sensors to determine whether or not the process is under control. We have been working with dynamic data from an oil production facility in the North sea, where unstable situations should be identified as soon as possible. Motivated by this problem setting, we propose a generative model for dynamic classification in continuous domains. At each time point the model can be seen as combining a naive Bayes model with a mixture of factor analyzers (FA). The latent variables of the FA are used to capture the dynamics in the process as well as modeling dependences between attributes.

M3 - Paper without publisher/journal

ER -

Zhong S, Martínez AM, Nielsen TD, Langseth H. Dynamic Latent Classification Model: Towards a More Expressive Model for Dynamic Classification. 2010. Afhandling præsenteret på The Florida Artificial Intelligence Research Society Conference, Daytona Beach, Florida, USA.