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Relevant bibliographies by topics / Bayesian network classifier / Dissertations / Theses

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Dissertations / Theses on the topic 'Bayesian network classifier'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

CODECASA, DANIELE. "Continuous time bayesian network classifiers." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/80691.

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Streaming data are relevant to finance, computer science, and engineering, while they are becoming increasingly important to medicine and biology. Continuous time Bayesian networks are designed for analyzing efficiently multivariate streaming data, exploiting the conditional independencies in continuous time homogeneous Markov processes. Continuous time Bayesian network classifiers are a specialization of continuous time Bayesian networks designed for multivariate streaming data classification when time duration of events matters and the class occurs in the future. Continuous time Bayesian net

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2

Mak, Lee Onn. "Identification and management of context using a Bayesian network classifier." Thesis, University of Surrey, 2004. http://epubs.surrey.ac.uk/842796/.

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The identification and management of context over time has become important in machine learning research over the last ten years. Although, there are established systems that have been created to detect changes successfully, there is room for further research to obtain an optimal system to both identify context and detect context changes from large datasets in an efficient manner. With the recent advancement of Bayesian network learning and Graphical presentation, this research extends the work by Schlimmer (118, 119), Widmer (34) and Harries (39) to derive context and detect the point of conc

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3

Thomas, Clifford S. "From 'tree' based Bayesian networks to mutual information classifiers : deriving a singly connected network classifier using an information theory based technique." Thesis, University of Stirling, 2005. http://hdl.handle.net/1893/2623.

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For reasoning under uncertainty the Bayesian network has become the representation of choice. However, except where models are considered 'simple' the task of construction and inference are provably NP-hard. For modelling larger 'real' world problems this computational complexity has been addressed by methods that approximate the model. The Naive Bayes classifier, which has strong assumptions of independence among features, is a common approach, whilst the class of trees is another less extreme example. In this thesis we propose the use of an information theory based technique as a mechanism f

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4

Tully, Philip. "Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits." Doctoral thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205568.

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Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and cons

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5

Tahseen, Tarkan. "A new approach to learning Bayesian network classifiers." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299378.

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6

Öhman, Oscar. "Rating corrumption within insurance companies using Bayesian network classifiers." Thesis, Umeå universitet, Statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160810.

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Bayesian Network (BN) classifiers are a type of probabilistic models. The learning process consists of two steps, structure learning and parameter learning. Four BN classifiers will be learned. These are two different Naive Bayes classifiers (NB), one Tree Augmented Naive Bayes classifier (TAN) and one Forest Naive Bayes classifier (FAN). The NB classifiers will utililize two different parameter learning techniques, which are generative learning and discriminative learning. Generative learning uses maximum likelihood estimation (MLE) to optimize the parameters, while discriminative learning us

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7

Taheri, Sona. "Learning Bayesian networks based on optimization approaches." Thesis, University of Ballarat, 2012. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/36051.

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Learning accurate classifiers from preclassified data is a very active research topic in machine learning and artifcial intelligence. There are numerous classifier paradigms, among which Bayesian Networks are very effective and well known in domains with uncertainty. Bayesian Networks are widely used representation frameworks for reasoning with probabilistic information. These models use graphs to capture dependence and independence relationships between feature variables, allowing a concise representation of the knowledge as well as efficient graph based query processing algorithms. This repr

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8

Chrysostomou, Kyriacos. "The role of classifiers in feature selection : number vs nature." Thesis, Brunel University, 2008. http://bura.brunel.ac.uk/handle/2438/3038.

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Wrapper feature selection approaches are widely used to select a small subset of relevant features from a dataset. However, Wrappers suffer from the fact that they only use a single classifier when selecting the features. The problem of using a single classifier is that each classifier is of a different nature and will have its own biases. This means that each classifier will select different feature subsets. To address this problem, this thesis aims to investigate the effects of using different classifiers for Wrapper feature selection. More specifically, it aims to investigate the effects of

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9

Kalkandara, Karolina. "Neural networks and classification trees for misclassified data." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312187.

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10

Ekdahl, Magnus. "Approximations of Bayes Classifiers for Statistical Learning of Clusters." Licentiate thesis, Linköping : Linköpings universitet, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5856.

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11

VILLA, SIMONE. "Continuous Time Bayesian Networks for Reasoning and Decision Making in Finance." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/69953.

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L'analisi dell'enorme quantità di dati finanziari, messi a disposizione dai mercati elettronici, richiede lo sviluppo di nuovi modelli e tecniche per estrarre efficacemente la conoscenza da utilizzare in un processo decisionale informato. Lo scopo della tesi concerne l'introduzione di modelli grafici probabilistici utilizzati per il ragionamento e l'attività decisionale in tale contesto. Nella prima parte della tesi viene presentato un framework che utilizza le reti Bayesiane per effettuare l'analisi e l'ottimizzazione di portafoglio in maniera olistica. In particolare, esso sfrutta, da un l

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12

Moyo, Lindani. "The classification performance of Bayesian Networks Classifiers: a case study of detecting Denial of Service (DoS) attacks in cloud computing environments." Thesis, University of Fort Hare, 2015. http://hdl.handle.net/10353/d1021327.

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In this research we propose a Bayesian networks approach as a promissory classification technique for detecting malicious traffic due to Denial of Service (DoS) attacks. Bayesian networks have been applied in numerous fields fraught with uncertainty and they have been proved to be successful. They have excelled tremendously in classification tasks i.e. text analysis, medical diagnoses and environmental modeling and management. The detection of DoS attacks has received tremendous attention in the field of network security. DoS attacks have proved to be detrimental and are the bane of cloud comp

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13

Ochuko, Rita E. "E-banking operational risk assessment. A soft computing approach in the context of the Nigerian banking industry." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5733.

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This study investigates E-banking Operational Risk Assessment (ORA) to enable the development of a new ORA framework and methodology. The general view is that E-banking systems have modified some of the traditional banking risks, particularly Operational Risk (OR) as suggested by the Basel Committee on Banking Supervision in 2003. In addition, recent E-banking financial losses together with risk management principles and standards raise the need for an effective ORA methodology and framework in the context of E-banking. Moreover, evaluation tools and / or methods for ORA are highly subjective,

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14

Thomas, Rodney H. "Machine Learning for Exploring State Space Structure in Genetic Regulatory Networks." Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1053.

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Genetic regulatory networks (GRN) offer a useful model for clinical biology. Specifically, such networks capture interactions among genes, proteins, and other metabolic factors. Unfortunately, it is difficult to understand and predict the behavior of networks that are of realistic size and complexity. In this dissertation, behavior refers to the trajectory of a state, through a series of state transitions over time, to an attractor in the network. This project assumes asynchronous Boolean networks, implying that a state may transition to more than one attractor. The goal of this project is to

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15

Ochuko, Rita Erhovwo. "E-banking operational risk assessment : a soft computing approach in the context of the Nigerian banking industry." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5733.

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This study investigates E-banking Operational Risk Assessment (ORA) to enable the development of a new ORA framework and methodology. The general view is that E-banking systems have modified some of the traditional banking risks, particularly Operational Risk (OR) as suggested by the Basel Committee on Banking Supervision in 2003. In addition, recent E-banking financial losses together with risk management principles and standards raise the need for an effective ORA methodology and framework in the context of E-banking. Moreover, evaluation tools and / or methods for ORA are highly subjective,

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16

Mézl, Martin. "Pokročilé dolování v datech v kardiologii." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217971.

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The aim of this master´s thesis is to analyse and search unusual dependencies in database of patients from Internal Cardiology Clinic Faculty Hospital Brno. The part of the work is theoretical overview of common data mining methods used in medicine, especially decision trees, naive Bayesian classifier, artificial neural networks and association rules. Looking for unusual dependencies between atributes is realized by association rules and naive Bayesian classifier. The output of this work is a complex system for Knowledge discovery in databases process for any data set. This work was realized w

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17

Svoboda, Pavel. "Metody klasifikace www stránek." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236643.

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The main goal of this master's thesis was to study the main principles of classification methods. Basic principles of knowledge discovery process, data mining and using an external class CSSBox are described. Special attantion was paid to implementation of a ,,k-nearest neighbors`` classification method. The first objective of this work was to create training and testing data described by 'n' attributes. The second objective was to perform experimental analysis to determine a good value for 'k', the number of neighbors.

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18

Lin, Kung-Ting, and 林冠廷. "Radial Basis Function Neural Network Based Bayesian Classifier Design for Data Fusion." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/29789234839137298174.

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碩士<br>育達商業科技大學<br>資訊管理所<br>100<br>A neural-network-based classifier design for adaptive Kalman filtering is introduced to fuse the measurements extracted from multiple sensors to improve tracking accuracy. The proposed method consists of a group of parallel Kalman filters and a classifier based on Radial Basis Function Neural Network (RBFNN). By incorporating Markov chain into Bayesian estimation scheme, a RBFNN is used as a probabilistic neural network for classification. Based upon data compression technique and on-line classification algorithm, an adaptive estimator to measurement fusion is

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19

Matthews, Jordan Lauren. "A Bayesian network classifier for quantifying design and performance flexibility with application to a hierarchical metamaterial design problem." Thesis, 2013. http://hdl.handle.net/2152/23566.

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Design problems in engineering are typically complex, and are therefore decomposed into a hierarchy of smaller, simpler design problems by the design management. It is often the case in a hierarchical design problem that an upstream design team’s achievable performance space becomes the design space for a downstream design team. A Bayesian network classifier is proposed in this research to map and classify a design team’s attainable performance space. The classifier will allow for enhanced collaboration between design teams, letting an upstream design team efficiently identify and share their

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20

Mohammadi, Mahnaz. "An Accelerator for Machine Learning Based Classifiers." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4245.

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Artificial Neural Networks (ANNs) are algorithmic techniques that simulate biological neural systems. Typical realization of ANNs are software solutions using High Level Languages (HLLs) such as C, C++, etc. Such solutions have performance limitations which can be attributed to one of the following reasons: • Code generated by the compiler cannot perform application specific optimizations. • Communication latencies between processors through a memory hierarchy could be significant due to non-deterministic nature of the communications. In data mining _eld, ANN algorithms have been widely used

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21

Shahan, David Williamson. "Bayesian network classifiers for set-based collaborative design." Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-12-2333.

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For many products, the design process is a complex system involving the interaction of many distributed design activities that need to be carefully coordinated. This research develops a new tool, called a Bayesian network classifier, to improve one specific aspect of this challenge: quantitatively capturing a consensus of which designs are feasible options for meeting system-wide engineering requirements. Classifiers enable designers to independently develop and share maps of the feasible regions of their design space, enabling set-based collaborative design. The method is set-based in that

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22

Nguyen, Son Thanh. "Advanced Bayesian Neural Network Classifiers of Head-movement Directions for Severely Disabled People." 2006. http://hdl.handle.net/2100/1054.

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University of Technology, Sydney. Faculty of Engineering.<br>Assistive technologies have been dedicated to providing additional accessibility to individuals who have physical or cognitive difficulties, impairments and disabilities, Various types of assistive technology products are also available on the market today. However, there are still a significant number of disabled people who are unable to use commercial assistive devices due to their high level of injury. For severely disabled people with quadriplegia resulting from high-level spinal-cord injuries or cerebral palsy, hands-free contro

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23

MacLean, Scott. "Automated recognition of handwritten mathematics." Thesis, 2014. http://hdl.handle.net/10012/8328.

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Most software programs that deal with mathematical objects require input expressions to be linearized using somewhat awkward and unfamiliar string-based syntax. It is natural to desire a method for inputting mathematics using the same two-dimensional syntax employed with pen and paper, and the increasing prevalence of pen- and touch-based interfaces causes this topic to be of practical as well as theoretical interest. Accurately recognizing two-dimensional mathematical notation is a difficult problem that requires not only theoretical advancement over the traditional theories of string-based l

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24

(9045878), Mitra Khanibaseri. "Developing Artificial Neural Networks (ANN) Models for Predicting E. Coli at Lake Michigan Beaches." Thesis, 2020.

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<p>A neural network model was developed to predict the E. Coli levels and classes in six (6) select Lake Michigan beaches. Water quality observations at the time of sampling and discharge information from two close tributaries were used as input to predict the E. coli. This research was funded by the Indiana Department of Environmental Management (IDEM). A user-friendly Excel Sheet based tool was developed based on the best model for making future predictions of E. coli classes. This tool will facilitate beach managers to take real-time decisions.</p> <p>The nowcast model was developed based

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25

Sleep, Julie. "Modelling of Level Crossing Accident Risk." 2008. http://arrow.unisa.edu.au:8081/1959.8/50702.

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This thesis details the development of a model of driver behaviour at railway level crossings that allows the probability of an accident under different conditions and interventions to be calculated. A method for classifying different crossings according to their individual risk levels is also described.

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