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Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks

J. M. Peña, J. A. Lozano, P. Larrañaga
2005 Evolutionary Computation  
of Bayesian networks.  ...  With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning  ...  Unsupervised Estimation of Bayesian Network Algorithm This section describes the unsupervised estimation of Bayesian network algorithm (UEBNA), whose only peculiarity with respect to existing EDAs is being  ... 
doi:10.1162/1063656053583432 pmid:15901426 fatcat:bel3pfh73vhzvfch73ydcxbtci

UNSUPERVISED LEARNING OF BAYESIAN NETWORKS VIA ESTIMATION OF DISTRIBUTION ALGORITHMS: AN APPLICATION TO GENE EXPRESSION DATA CLUSTERING

J. M. PEÑA, J. A. LOZANO, P. LARRAÑAGA
2004 International Journal of Uncertainty Fuzziness and Knowledge-Based Systems  
This paper proposes using estimation of distribution algorithms for unsupervised learning of Bayesian networks, directly as well as within the framework of the Bayesian structural EM algorithm.  ...  The validation of the clusters of genes that are identified suggests that these may be biologically meaningful.  ...  Larrañaga were supported by the Spanish Ministry of Science and Technology under grant TIC2001-2973-C05-03.  ... 
doi:10.1142/s0218488504002588 fatcat:dycbnv3sfnbtrihd4khzutorcm

Page 8293 of Mathematical Reviews Vol. , Issue 2001K [page]

2001 Mathematical Reviews  
techniques, and also from the point of view of its application to four major types of unsupervised learning tasks and three major types of supervised learning networks.  ...  A general learning problem is formulated: in terms of building a pair of complemen- tary models based on Bayesian decomposition representations of the joint distribution on the space in which sample data  ... 

Computational Statistics and Predictive Analysis in Machine Learning

2016 International Journal of Science and Research (IJSR)  
Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.  ...  Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible.  ...   Bayesian Networks A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies  ... 
doi:10.21275/v5i1.nov152818 fatcat:pbmkgtmrcrenhnjvxju6pzpjqa

Minimally-Supervised Attribute Fusion for Data Lakes [article]

Karamjit Singh, Garima Gupta, Gautam Shroff, Puneet Agarwal
2017 arXiv   pre-print
In this paper, we present an ensemble model combining minimal supervision using Bayesian network models together with unsupervised textual matching for automating such 'attribute fusion'.  ...  Traditional record-linkage techniques are typically unsupervised, relying textual similarity features across attributes to estimate matches.  ...  Supervised Bayesian Model Approach to build SBM comprises of:(1) Network Structure Learning, (2) Parameter Learning, & (3) Bayesian Inference.  ... 
arXiv:1701.01094v1 fatcat:wlkuese72bclhmvx2v7xhcxmmi

Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network

Syed Hasib Akhter Faruqui, Adel Alaeddini, Carlos A. Jaramillo, Jennifer S. Potter, Mary Jo Pugh, Mark Webber Miller
2018 PLoS ONE  
Department of Veterans Affairs for a period of five years, we compare the performance of the proposed unsupervised Bayesian network in comparison with those of Bayesian networks developed based on supervised  ...  To improve the efficiency of the learning process, we use an extension of maximum weight spanning tree algorithm and greedy search algorithm to study the structure of the proposed network in three stages  ...  Generally, the Bayesian network structure can be learned using: (1) Unsupervised learning algorithms, including score-based and constraintbased algorithms, (2) Supervised learning algorithms, where expert  ... 
doi:10.1371/journal.pone.0199768 pmid:30001371 pmcid:PMC6042705 fatcat:pzdp7ppjtrg2vplwewkmtmutkm

Unsupervised training of Bayesian networks for data clustering

D. T. Pham, G. A. Ruz
2009 Proceedings of the Royal Society A  
This paper presents a new approach to the unsupervised training of Bayesian network classifiers.  ...  Three models have been analysed: the Chow and Liu (CL) multinets; the treeaugmented naive Bayes; and a new model called the simple Bayesian network classifier, which is more robust in its structure learning  ...  The authors would like to thank the EU-funded I*PROMS Network of Excellence and the ORS Award for financially supporting this research.  ... 
doi:10.1098/rspa.2009.0065 fatcat:62tct7urgncsrdtvrl23qgcjqm

Unsupervised Learning [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
An Unsupervised Learning Result: A result to complement the algorithm given by Bernhard Sch olkopf was presented, showing that frequentist bounds can be derived for estimating the support of a distribution  ...  I discuss a simple toy model of unsupervised learning which is formulated in the framework of density estimation.  ... 
doi:10.1007/978-1-4899-7687-1_976 fatcat:f665z5u2enh27hdq7nrzcugyuq

Damage assessment of smart composite structures via machine learning: a review

Asif Khan, Nayeon Kim, Jae Kyong Shin, Heung Soo Kim, Byeng Dong Youn
2019 JMST Advances  
This article focuses on a review of discriminative features and the corresponding machine learning algorithms (both supervised and unsupervised), for various types of damage in smart composite structures  ...  In addition, one machine learning algorithm may show optimum performance for the discriminative features of a particular problem but fails for others.  ...  In general, supervised learning techniques are used to deal with the problems of classification and regression, whereas density estimation is carried out via unsupervised learning body.  ... 
doi:10.1007/s42791-019-0012-2 fatcat:xp423kbqhba57pxmyxavuekm34

Bayesian Learning of Probabilistic Dipole Inversion for Quantitative Susceptibility Mapping [article]

Jinwei Zhang, Hang Zhang, Mert Sabuncu, Pascal Spincemaille, Thanh Nguyen, Yi Wang
2020 arXiv   pre-print
In PDI, such CNN is firstly trained on healthy subjects dataset with labels by maximizing the posterior Gaussian distribution loss function as used in Bayesian deep learning.  ...  A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve quantitative susceptibility mapping (QSM) inverse problem in MRI with uncertainty estimation  ...  Figure 1: Network architecture of the proposed method. Dual decoders' outputs represent mean and variance maps. COSMOS dataset was used to do supervised Bayesian training via MAP in Eq. 9.  ... 
arXiv:2004.12573v1 fatcat:doixboxtnjafpo7waly5vaq7uy

Uncertainty-aware Cardinality Estimation by Neural Network Gaussian Process [article]

Kangfei Zhao, Jeffrey Xu Yu, Zongyan He, Hao Zhang
2021 arXiv   pre-print
This special class of BDL, known as Neural Network Gaussian Process (NNGP), inherits the advantages of Bayesian approach while keeping universal approximation of neural network, and can utilize a much  ...  We employ Bayesian deep learning (BDL), which serves as a bridge between Bayesian inference and deep learning.The prediction distribution by BDL provides principled uncertainty calibration for the prediction  ...  Acknowledgement We thank Zongheng Yang, the author of NeuroCard [66] for his help in testing the estimator.  ... 
arXiv:2107.08706v1 fatcat:t35s7o5x6ve5hdw2mcxxd32jki

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges [article]

Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok-Lim Alvin Yau, Yehia Elkhatib, Amir Hussain, Ala Al-Fuqaha
2017 arXiv   pre-print
The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking.  ...  Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly  ...  Location estimation using Bayesian network in LAN is discussed in [115] .  ... 
arXiv:1709.06599v1 fatcat:llcg6gxgpjahha6bkhsitglrsm

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation [article]

Adrian V. Dalca, Evan Yu, Polina Golland, Bruce Fischl, Mert R. Sabuncu, Juan Eugenio Iglesias
2019 arXiv   pre-print
However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensitive to the intensity profiles in the training dataset.  ...  Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms.  ...  BF's interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies.  ... 
arXiv:1904.11319v2 fatcat:q3syhlr6vbav3mvobg2f2epmoy

Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction [article]

Qiaoqiao Ding, Hui Ji, Yuhui Quan, Xiaoqun Zhang
2022 arXiv   pre-print
The proposed method is built on a re-parametrization technique for Bayesian inference via deep network with random weights, combined with additional total variational (TV) regularization.  ...  Aiming at addressing the challenges raised by the collection of training dataset, this paper proposed a unsupervised deep learning method for LDCT image reconstruction, which does not require any external  ...  Discussion and Conclusion In this paper, we proposed an unsupervised learning method for LDCT image reconstruction, which is based on a re-parametrization via the network with For the layers from i = 1  ... 
arXiv:2205.00463v1 fatcat:fu3yen6cdrfmfpiy6l3z4qqrgy

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation [chapter]

Adrian V. Dalca, Evan Yu, Polina Golland, Bruce Fischl, Mert R. Sabuncu, Juan Eugenio Iglesias
2019 Lecture Notes in Computer Science  
However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensitive to the intensity profiles in the training dataset.  ...  Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms.  ...  BF's interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies.  ... 
doi:10.1007/978-3-030-32248-9_40 pmid:32432231 pmcid:PMC7235150 fatcat:skx3u7lixjc4vdw42n3ljacpwa
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