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Graphic Symbol Recognition Using Graph Based Signature and Bayesian Network Classifier

Muhammad Muzzamil Luqman, Thierry Brouard, Jean-Yves Ramel
2009 2009 10th International Conference on Document Analysis and Recognition  
We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition.  ...  This signature corresponds to geometry and topology of the symbol.  ...  We discretize our learning and test datasets because the Bayesian network algorithms, which we have used, require discrete data.  ... 
doi:10.1109/icdar.2009.92 dblp:conf/icdar/LuqmanBR09 fatcat:tyk2kvhjozdtlfbt4tzzbppmge

An examination of the effect of discretization on a nave Bayes models performance
English

Aghaei Chadegani Arezoo, Poursina Davood
2013 Scientific Research and Essays  
A Bayesian network (or a belief network) is a probabilistic graphical model that represents a set of variables and their probabilistic independencies.  ...  During the discretization process, one problem that researchers faced is to decide the number of states for discretization. Does the number of states chosen for discretization impact models' power?  ...  Bayesian networks are powerful tools both for graphical representation of the relationships among a set of variables and for dealing with uncertainties in expert systems (Pearl, 1988) .  ... 
doi:10.5897/sre09.174 fatcat:kjdoag5wjrbxbmje34xcc3wssi

Page 3316 of Mathematical Reviews Vol. , Issue 2004d [page]

2004 Mathematical Reviews  
In this paper, we establish an association between the structural complexity of Bayesian networks and their representational power.  ...  Summary: “One of the most important fundamental properties of Bayesian networks is the representational power, reflecting what kind of functions they can or cannot represent.  ... 

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition [chapter]

Muhammad Muzzamil Luqman, Mathieu Delalandre, Thierry Brouard, Jean-Yves Ramel, Josep Lladós
2010 Lecture Notes in Computer Science  
The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols.  ...  The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural  ...  can obtain a more powerful Bayesian network classifier.  ... 
doi:10.1007/978-3-642-13728-0_2 fatcat:on3dmxbd7jakdo3ooo6fxwuoni

Importance Sampling for General Hybrid Bayesian Networks

Changhe Yuan, Marek J. Druzdzel
2007 Journal of machine learning research  
Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability  ...  This paper describes an importance sampling-based algorithm that directly deals with hybrid Bayesian networks constructed in the most general settings and guarantees to converge to the correct answers  ...  Acknowledgements This research was supported by the Air Force Of ce of Scienti c Research grants F496200310187 and FA9550 0610243 and by Intel Research.  ... 
dblp:journals/jmlr/YuanD07 fatcat:omevadxlt5ck7efb3ore5wko44

Markovian Approach to Time Transition Inference on Bayesian Networks [chapter]

Adamo Santana, Diego Cardoso, Carlos Renato, Joo Cost
2010 Bayesian Network  
BNs are more known and popularised by the name of Bayesian networks.  ...  Hence the use of Bayesian networks to codify the probabilistic relations of the variables and to make inferences on the conditions of the power system from the historical consumption and its correlation  ...  It contains recent developments in the field and illustrates, on a sample of applications, the power of Bayesian networks in dealing the modeling of complex systems.  ... 
doi:10.5772/10072 fatcat:pzyx2hj7azf4fo3jcm4l2h6t3a

Automated uncertainty quantification analysis using a system model and data

Saideep Nannapaneni, Sankaran Mahadevan, David Lechevalier, Anantha Narayanan, Sudarsan Rachuri
2015 2015 IEEE International Conference on Big Data (Big Data)  
The actual Bayesian network is an instance model of the Bayesian network meta-model.  ...  The proposed methodology involves creating a meta-model for the Bayesian network using GME and a syntax representation for the conditional probability tables/ distributions.  ...  ACKNOWLEDGMENT The research reported in this paper was funded in part by the National Institute of Standards and Technology under Cooperative Agreements No. 70NANB14H036 and No. 70NANB13H159, and NIST's  ... 
doi:10.1109/bigdata.2015.7363901 dblp:conf/bigdataconf/NannapaneniMLNR15 fatcat:djuzr7syevb3fhaibklzdottvu

Employing fuzzy intervals and loop-based methodology for designing structural signature: an application to symbol recognition [article]

Muhammad Muzzamil Luqman, Mathieu Delalandre, Thierry Brouard, Jean-Yves Ramel, Josep Lladós
2010 arXiv   pre-print
The joint probability distribution of signatures is encoded by a Bayesian network.  ...  Finally we deploy the Bayesian network in supervised learning scenario for recognizing query symbols.  ...  We believe that the power of Bayesian networks is not fully explored; as instead of using pre-defined dependency relationships we can obtain a better Bayesian network classifier if we find dependencies  ... 
arXiv:1004.5427v1 fatcat:ytssdgtbireqpobtk5my2urmcy

Reasoning with discrete factor graph

Indar Sugiarto, Paul Maier, Jorg Conradt
2013 2013 International Conference on Robotics, Biomimetics, Intelligent Computational Systems  
When working with probabilistic graphical models we usually have two options to build the model: either using a Bayesian network (BN) or a Markov random field (MRF).  ...  However, there exist one more graphical representation which is able to unify the properties of BN and MRF that is called Factor Graph.  ...  any value in the nodes into some discrete representation.  ... 
doi:10.1109/robionetics.2013.6743599 fatcat:kezbfxgmgvhppnpw2hv3qkgoh4

Optimization Strategies for Improving the Interpretability of Bayesian Networks: an Application in Power Systems [chapter]

Claudio Rocha, Diego Cardoso, Adamo Santana, Carlos Renato
2010 Bayesian Network  
www.intechopen.com Optimization strategies for improving the interpretability of bayesian networks: an application in power systems 267 BN is known to offer, given its knowledge representation formalism  ...  improving the interpretability of bayesian networks: an application in power systems 271 P(x i | e 1 ,e 2 ,...  ...  It contains recent developments in the field and illustrates, on a sample of applications, the power of Bayesian networks in dealing the modeling of complex systems.  ... 
doi:10.5772/10065 fatcat:4uzxtogd6naxbfq3uhe7az4vja

Neural Architecture Optimization with Graph VAE [article]

Jian Li, Yong Liu, Jiankun Liu, Weiping Wang
2020 arXiv   pre-print
The mapping of network representation from the discrete space to a latent space is the key to discovering novel architectures, however, existing gradient-based methods fail to fully characterize the networks  ...  The encoder and the decoder belong to a graph VAE, mapping architectures between continuous representations and network architectures.  ...  Although the graph embedding h out extracts structural information of neural networks, it is hard to optimized due to the discrete representations. Remark 1.  ... 
arXiv:2006.10310v1 fatcat:knuhg6vd6fei5bxtrri2mcx2tq

Bayesian Network Integrated Testing Strategy and beyond
EN

Federico Stefanini
2013 ALTEX: Alternatives to Animal Experimentation  
Nevertheless, some issues deserve further refinement to unleash the full power of the Bayesian paradigm in ItS and to put the "Bayesian Network Integrated testing Strategy" in perspective.  ...  Similarly, mixed Bayesian networks made by discrete and Gaussian variables admit FePes if Gaussian variables are never parents of discrete variables (Cowell et al., 1999) . the generality of Bayesian  ... 
doi:10.14573/altex.2013.3.386 pmid:23861081 fatcat:bhz5xta4bzdndezkm2akm3r34m

Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

Jose Ignacio Aizpurua, Victoria M. Catterson, Brian G. Stewart, Stephen D. J. McArthur, Brandon Lambert, Bismark Ampofo, Gavin Pereira, James G. Cross
2018 IEEE transactions on dielectrics and electrical insulation  
ABSTRACT Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid.  ...  2018) Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing. IEEE Transactions on Dielectrics and Electrical Insulation, 25 (2).  ...  Bayesian networks are a compact representation of joint probability distributions [17] .  ... 
doi:10.1109/tdei.2018.006766 fatcat:q2xtzvcrtbeidgkzer3slx3orm

The use of Bayesian Networks in Detecting the States of Ventilation Mills in Power Plants

Sanja Vujnović, Predrag Todorov, Željko Đurović, Aleksandra Marjanović
2014 Electronics  
The main objective of this paper is to present a new method of predictive maintenance which can detect the states of coal grinding mills in thermal power plants using Bayesian networks.  ...  This method uses acoustic signals and statistical signal pre-processing tools to compute the inputs of the Bayesian network.  ...  During the last decade Bayesian networks have become a very powerful tool for representation of complex C systems and it has been used in many areas of research including fault detection and fault isolation  ... 
doi:10.7251/els1418016v fatcat:k5iojotkn5a33liigj3aap6uyq

Belief update in CLG Bayesian networks with lazy propagation

A.L. Madsen
2008 International Journal of Approximate Reasoning  
In recent years, Bayesian networks with a mixture of continuous and discrete variables have received an increasing level of attention.  ...  In this paper, we focus on the restricted class of mixture Bayesian networks known as conditional linear Gaussian Bayesian networks (CLG Bayesian networks) and present an architecture for exact belief  ...  Acknowledgement This paper is an extended and revised version of [15] .  ... 
doi:10.1016/j.ijar.2008.05.001 fatcat:cqhkcskbxnbt7nhe73327xqtyq
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