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Classification-oriented structure learning in Bayesian networks for multimodal event detection in videos

Guillaume Gravier, Claire-Hélène Demarty, Siwar Baghdadi, Patrick Gros
2012 Multimedia tools and applications  
We investigate the use of structure learning in Bayesian networks for a complex multimodal task of action detection in soccer videos.  ...  We illustrate that classical score-oriented structure learning algorithms, such as the K2 one whose usefulness has been demonstrated on simple tasks, fail in providing a good network structure for classification  ...  Acknowledgments This work was partially funded by OSEO, French state agency for innovation, in the framework of the Quaero project.  ... 
doi:10.1007/s11042-012-1169-y fatcat:dgjjd5agwnhzdnld4pfe6nkova

Improving Classification In Bayesian Networks Using Structural Learning

Hong Choon Ong
2011 Zenodo  
In this study, the use of structural learning in Bayesian Network is proposed to be applied where there are relationships between the features when using the Naïve Bayes.  ...  However, Naïve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem.  ...  Several algorithms have been proposed for the inductive learning of general Bayesian network.  ... 
doi:10.5281/zenodo.1335261 fatcat:4i547utuirg6ljckulexixnqsa

Learning Bayesian Belief Network Classifiers: Algorithms and System [chapter]

Jie Cheng, Russell Greiner
2001 Lecture Notes in Computer Science  
This paper investigates the methods for learning predictive classifiers based on Bayesian belief networks (BN) -primarily unrestricted Bayesian networks and Bayesian multi-nets.  ...  We present our algorithms for learning these classifiers, and discuss how these methods address the overfitting problem and provide a natural method for feature subset selection.  ...  Learning Unrestricted BN Classifiers This section presents algorithms for learning general Bayesian networks and Bayesian multi-nets.  ... 
doi:10.1007/3-540-45153-6_14 fatcat:rvjewedkhbbqtinjtpaoqthga4

Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite

B. Yang, X. Yu
2014 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains.  ...  In this paper, we apply Bayesian Networks Augmented Naive Bayes (BAN) to texture classification of High-resolution satellite images and put up a new method to construct the network topology structure in  ...  The authors wish to thank the anonymous reviewers for the comments and suggestions on this paper.  ... 
doi:10.5194/isprsarchives-xl-4-325-2014 fatcat:xpb4xxebfvdbzawdhnsmh2f3xi

A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms

Wei Zhou, Gengxin Sun
2022 Discrete Dynamics in Nature and Society  
In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms.  ...  In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based on Bayesian algorithm theory.  ...  A hybrid constraint-based structure learning method will be given for the shortcomings of the evolutionary computation-based Bayesian network structure learning method.  ... 
doi:10.1155/2022/8998352 fatcat:nzjoighvwnhmveq5fr74krk6ru

An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks [article]

Anika Tabassum, Naimul Khan
2020 arXiv   pre-print
We propose a modular network architecture, where a traditional neural network is used as a feature extractor with transfer learning, followed by a simple Bayesian neural network.  ...  Utilizing a two-stage approach helps reducing the computational complexity, making the proposed framework attractive for wider deployment.  ...  For example, when using an adapted version of ResNet, the feature generator network will be the portion of the network before the fully connected layers start, and the smaller network for Bayesian learning  ... 
arXiv:2008.05566v1 fatcat:abaoazm2dzdqlixv63aj2iozza

Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

Qing Zhou, Ling He, PengFei Lu, Yansong Wang
2017 MATEC Web of Conferences  
In order to ensure the fastness and convenience sensorless drive diagnosis, in this paper, the classic Bayesian network learning algorithm-K2 algorithm is used to study the network structure of each feature  ...  Sensorless Drive Diagnosis can be used to assess the process data without the need for additional costintensive sensor technology, and you can understand the synchronous motor and connecting parts of the  ...  It can be seen that the time required for learning the Bayesian network structure ( train T ) is significantly shortened, and the accuracy of classification is not reduced.  ... 
doi:10.1051/matecconf/201712802017 fatcat:p24pv75esjgr7m7z76xdzpge4q

Inference and Learning in Multi-dimensional Bayesian Network Classifiers [chapter]

Peter R. de Waal, Linda C. van der Gaag
2007 Lecture Notes in Computer Science  
We describe the family of multi-dimensional Bayesian network classifiers which include one or more class variables and multiple feature variables.  ...  We further describe the learning problem for the subfamily of fully polytree-augmented multi-dimensional classifiers and show that its computational complexity is polynomial in the number of feature variables  ...  In the present paper, we address the computational complexity of the classification problem for multi-dimensional Bayesian network classifiers.  ... 
doi:10.1007/978-3-540-75256-1_45 fatcat:f6jp5ozco5h6dk3pvrmyui4jmy

Bayesian network structure learning and inference in indoor vs. outdoor image classification

M.J. Kane, A. Savakis
2004 Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.  
set for Bayesian network structure learning.  ...  Bayesian network model selection techniques may be used to learn and elucidate conditional relationships between features in pattern recognition tasks.  ...  In this paper, we propose the use of network structure learning for scene classification.  ... 
doi:10.1109/icpr.2004.1334268 dblp:conf/icpr/KaneS04 fatcat:2p7tselpzzg3vhmjvq7bjwadrm

Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification [article]

Brian D. Ziebart, Anind K. Dey, J Andrew Bagnell
2012 arXiv   pre-print
We also apply SCFs to Bayes Net classification to learn selective forest augmented Naive Bayes classifiers.  ...  We apply SCFs to temporal data to learn Dynamic Bayesian Networks having an intra-timestep forest and inter-timestep limited in-degree structure, improving model accuracy over DBNs without the combination  ...  One common approach to structure learning is to find the maximum a posteriori (MAP) estimate for the graph structure of the Bayesian Network.  ... 
arXiv:1206.5281v1 fatcat:opvz5pug6bcqlmc2vpr2ebxyu4

Stochastic margin-based structure learning of Bayesian network classifiers

Franz Pernkopf, Michael Wohlmayr
2013 Pattern Recognition  
We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers.  ...  Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms  ...  Acknowledgments Thanks to Jeff Bilmes for discussions and support in writing this paper. This work was supported by the Austrian Science Fund (FWF) under the project P22488-N23) and S10610-N13).  ... 
doi:10.1016/j.patcog.2012.08.007 pmid:24511159 pmcid:PMC3914412 fatcat:3d2foeps5zavlflwrmbk7i6qoi

Boosted Bayesian network classifiers

Yushi Jing, Vladimir Pavlović, James M. Rehg
2008 Machine Learning  
The use of Bayesian networks for classification problems has received a significant amount of recent attention.  ...  We also demonstrate that structure learning is beneficial in the construction of boosted Bayesian network classifiers.  ...  Boosted parameter learning Ensemble model Instead of maximizing the CLL score for a single Bayesian network model, we take the ensemble approach and maximize the classification performance of the ensemble  ... 
doi:10.1007/s10994-008-5065-7 fatcat:mnophkkpnzfgdevfmzpiy26tzy

An Evolutionary Bayesian Network Learning Algorithm using Feature Subset Selection for Bayesian Network Classifiers

Shefali K.
2016 International Journal of Computer Applications  
But learning an optimal Bayesian belief network for a Bayesian network classifier is a NPhard problem.  ...  Classification is the process of constructing (learning) a model (classifier) to predict the class (labels) for given data.  ...  As learning the optimal Bayesian Network classifier has been shown to be NP-hard, and due to computational complexity of the current state of the art Bayesian Network learning algorithms for Bayesian network  ... 
doi:10.5120/ijca2016908057 fatcat:djby6377jreyznri4qzkud2vfm

Classification of Down Syndrome of Mice Protein Dataset on MongoDB Database

Fahriye Gemci Furat, Turgay Ibrikci
2018 Balkan Journal of Electrical and Computer Engineering  
The classification of the data set was succeeded with 94.3519% accuracy in 0.06 seconds using Bayesian Network, with 99.2593% accuracy in 0.01 seconds using KNN, with 95.4630 % accuracy in 1.2 seconds  ...  On the other hand, the classification of the data set was succeeded with 95.3704% accuracy in 0.22 seconds using Bayesian Network, with 98.3333% accuracy in 0 seconds using KNN, with 98.3333% accuracy  ...  Bayesian Network that is one of them classification methods has been remarkably successful in many studies for classification such as [4] [5] [6] [7] [7] .  ... 
doi:10.17694/bajece.419553 fatcat:rckc4ew6bnc77fmcmcdupi275a

Learning Bayesian classifiers using overlapping swarm intelligence

Nathan Fortier, John Sheppard, Shane Strasser
2014 2014 IEEE Symposium on Swarm Intelligence  
In this paper, we propose a novel approximation algorithm for learning Bayesian network classifiers based on Overlapping Swarm Intelligence.  ...  However, structure learning of Bayesian networks has been shown to be NP-Hard.  ...  In the area of classification, model learning typically consists of learning relationships between the various features in the classification problem.  ... 
doi:10.1109/sis.2014.7011796 dblp:conf/swis/FortierSS14 fatcat:doixx3sj35bwppmjsmdw7jb2qq
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