Variational Bayesian Approach for Long-Term Relevance Feedback

Advances in Knowledge Discovery and Data Mining - Lecture Notes in Computer Science ◽

10.1007/978-3-642-01307-2_31 ◽

2009 ◽

pp. 327-338

Author(s):

Sabri Boutemedjet ◽

Djemel Ziou

Keyword(s):

Bayesian Approach ◽

Relevance Feedback ◽

Variational Bayesian

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A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data

Engineering Structures ◽

10.1016/j.engstruct.2020.110520 ◽

2020 ◽

Vol 212 ◽

pp. 110520 ◽

Cited By ~ 5

Author(s):

Y.Q. Ni ◽

Y.W. Wang ◽

C. Zhang

Keyword(s):

Structural Health Monitoring ◽

Bayesian Approach ◽

Health Monitoring ◽

Condition Assessment ◽

Monitoring Data ◽

Expansion Joints ◽

Structural Health

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Increasing the mixture components of non-uniform HMM structures based on a variational Bayesian approach

10.21437/interspeech.2004-274 ◽

2004 ◽

Author(s):

Takatoshi Jitsuhiro ◽

Satoshi Nakamura

Keyword(s):

Bayesian Approach ◽

Variational Bayesian

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Variational Bayesian learning for Gaussian mixture HMM in seizure prediction based on long term EEG of epileptic rats

2014 21th Iranian Conference on Biomedical Engineering (ICBME) ◽

10.1109/icbme.2014.7043909 ◽

2014 ◽

Cited By ~ 2

Author(s):

S. Esmaeili ◽

B. N. Araabi ◽

H. Soltanian-Zadeh ◽

L. Schwabe

Keyword(s):

Bayesian Learning ◽

Gaussian Mixture ◽

Seizure Prediction ◽

Variational Bayesian

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A variational bayesian approach to compressive sensing based on Double Lomax priors

2013 IEEE International Conference on Acoustics, Speech and Signal Processing ◽

10.1109/icassp.2013.6638815 ◽

2013 ◽

Cited By ~ 3

Author(s):

Xiaojing Gu ◽

Henry Leung ◽

Xingsheng Gu

Keyword(s):

Compressive Sensing ◽

Bayesian Approach ◽

Variational Bayesian

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Smooth Performance Landscapes of the Variational Bayesian Approach

Artificial Neural Networks: Biological Inspirations – ICANN 2005 - Lecture Notes in Computer Science ◽

10.1007/11550907_38 ◽

2005 ◽

pp. 241-246

Author(s):

Zhuo Gao ◽

K. Y. Michael Wong

Keyword(s):

Bayesian Approach ◽

Variational Bayesian

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A Variational Bayesian Approach for Order Tracking in Rotating Machines

Lecture Notes in Mechanical Engineering - Advances in Condition Monitoring and Structural Health Monitoring ◽

10.1007/978-981-15-9199-0_3 ◽

2021 ◽

pp. 25-33

Author(s):

Y. Marnissi ◽

D. Abboud ◽

M. Elbadaoui

Keyword(s):

Bayesian Approach ◽

Rotating Machines ◽

Variational Bayesian ◽

Order Tracking

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Online Image Retrieval System Using Long Term Relevance Feedback

Lecture Notes in Computer Science - Image and Video Retrieval ◽

10.1007/11788034_43 ◽

2006 ◽

pp. 422-431 ◽

Cited By ~ 1

Author(s):

Lutz Goldmann ◽

Lars Thiele ◽

Thomas Sikora

Keyword(s):

Image Retrieval ◽

Relevance Feedback ◽

Retrieval System ◽

Image Retrieval System

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A Variational Bayesian Approach for Unsupervised Clustering

Lecture Notes in Electrical Engineering - Frontier Computing ◽

10.1007/978-981-10-0539-8_63 ◽

2016 ◽

pp. 651-660 ◽

Cited By ~ 1

Author(s):

Mu-Song Chen ◽

Hsuan-Fu Wang ◽

Chi-Pan Hwang ◽

Tze-Yee Ho ◽

Chan-Hsiang Hung

Keyword(s):

Bayesian Approach ◽

Unsupervised Clustering ◽

Variational Bayesian

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A hybrid stochastic model based Bayesian approach for long term energy demand managements

Energy Strategy Reviews ◽

10.1016/j.esr.2020.100462 ◽

2020 ◽

Vol 28 ◽

pp. 100462

Author(s):

Somayeh Ahmadi ◽

Amir hossien Fakehi ◽

Ali vakili ◽

Morteza Haddadi ◽

Seyed Hossein Iranmanesh

Keyword(s):

Stochastic Model ◽

Bayesian Approach ◽

Energy Demand ◽

Model Based ◽

Term Energy

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A Bayesian approach to flow record infilling and extension for reservoir design

Hydrology and Earth System Sciences ◽

10.5194/hess-3-491-1999 ◽

1999 ◽

Vol 3 (4) ◽

pp. 491-503 ◽

Cited By ~ 1

Author(s):

D. A. Jones ◽

K. J. Sene

Keyword(s):

Bayesian Approach ◽

Sampling Procedure ◽

Rainfall Data ◽

Generation Process ◽

Additional Information ◽

Regional Flow ◽

Flow Generation ◽

Reservoir Design ◽

New Procedures

Abstract. A Bayesian approach is described for dealing with the problem of infilling and generating stochastic flow sequences using rainfall data to guide the flow generation process, and including bounded (censored) observed flow and rainfall data to provide additional information. Solutions are obtained using a Gibbs sampling procedure. Particular problems discussed include developing new procedures for fitting transformations when bounded values are available, coping with additional information in the form of values, or bounds, for totals of flows across several sites, and developing relationships between annual flow and rainfall data. Examples are shown of both infilled values of unknown past river flows, with assessment of uncertainty, and realisations of flows representative of what might occur in the future. Several procedures for validating the model output are described and the central estimates of flows, taken as a surrogate for historical observed flows, are compared with long term regional flow and rainfall data.

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