Relevant bibliographies by topics / Machine learning models / Dissertations / Theses
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Dissertations / Theses on the topic 'Machine learning models'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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1

Lounici, Sofiane. "Watermarking machine learning models." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS282.pdf.

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La protection de la propriété intellectuelle des modèles d’apprentissage automatique apparaît de plus en plus nécessaire, au vu des investissements et de leur impact sur la société. Dans cette thèse, nous proposons d’étudier le tatouage de modèles d’apprentissage automatique. Nous fournissons un état de l’art sur les techniques de tatouage actuelles, puis nous le complétons en considérant le tatouage de modèles au-delà des tâches de classification d’images. Nous définissons ensuite les attaques de contrefaçon contre le tatouage pour les plateformes d’hébergement de modèles, et nous présentons
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Hardoon, David Roi. "Semantic models for machine learning." Thesis, University of Southampton, 2006. https://eprints.soton.ac.uk/262019/.

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In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature selection techniques in machine learning applications. The applied approaches obtain new feature directions on data, such that machine learning applications would show an increase in performance. We review three principle methods that are used throughout the thesis. Firstly Canonical Correlation Analysis (CCA), which is a method of correlating linear relationships between two multidimensional variables
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Bone, Nicholas. "Models of programs and machine learning." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244565.

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Zhu, Xiaodan. "On Cross-Series Machine Learning Models." W&M ScholarWorks, 2020. https://scholarworks.wm.edu/etd/1616444550.

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Sparse high dimensional time series are common in industry, such as in supply chain demand and retail sales. Accurate and reliable forecasting of high dimensional time series is essential for supply chain planning and business management. In practical applications, sparse high dimensional time series prediction faces three challenges: (1) simple models cannot capture complex patterns, (2) insufficient data prevents us from pursuing more advanced models, and (3) time series in the same dataset may have widely different properties. These challenges prevent the currently prevalent models and theo
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Amerineni, Rajesh. "BRAIN-INSPIRED MACHINE LEARNING CLASSIFICATION MODELS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1806.

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This dissertation focuses on the development of three classes of brain-inspired machine learning classification models. The models attempt to emulate (a) multi-sensory integration, (b) context-integration, and (c) visual information processing in the brain.The multi-sensory integration models are aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli. Two multimodal classification models are introduced: the feature integrating (FI) model and the decision integrating (DI) model. The FI model, inspired by multisensory integration in the subc
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MARRAS, MIRKO. "Machine Learning Models for Educational Platforms." Doctoral thesis, Università degli Studi di Cagliari, 2020. http://hdl.handle.net/11584/285377.

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Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to e
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Kim, Been. "Interactive and interpretable machine learning models for human machine collaboration." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98680.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 135-143).<br>I envision a system that enables successful collaborations between humans and machine learning models by harnessing the relative strength to accomplish what neither can do alone. Machine learning techniques and humans have skills that complement each other - machine learning techniques are good at computation on data at the lowest level of granularity, whereas people are better at abstracting
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Shen, Chenyang. "Regularized models and algorithms for machine learning." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/195.

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Multi-lable learning (ML), multi-instance multi-label learning (MIML), large network learning and random under-sampling system are four active research topics in machine learning which have been studied intensively recently. So far, there are still a lot of open problems to be figured out in these topics which attract worldwide attention of researchers. This thesis mainly focuses on several novel methods designed for these research tasks respectively. Then main difference between ML learning and traditional classification task is that in ML learning, one object can be characterized by several
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Ahlin, Mikael, and Felix Ranby. "Predicting Marketing Churn Using Machine Learning Models." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-161408.

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For any organisation that engages in marketing actions there is a need to understand how people react to communication messages that are sent. Since the introduction of General Data Protection Regulation, the requirements for personal data usage have increased and people are able to effect the way their personal information is used by companies. For instance people have the possibility to unsubscribe from communication that is sent, this is called Opt-Out and can be viewed as churning from communication channels. When a customer Opt-Out the organisation loses the opportunity to send personalis
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BALLANTE, ELENA. "Statistical and Machine Learning models for Neurosciences." Doctoral thesis, Università degli studi di Pavia, 2021. http://hdl.handle.net/11571/1447634.

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This thesis addresses several problems encountered in the field of statistical and machine learning methods for data analysis in neurosciences. The thesis is divided into three parts. The first part of the thesis is related to classification tree models. In the research field of polarization measures, a new polarization measure is defined. The function is incorporated in the decision tree algorithm as a splitting function in order to tackle some weaknesses of classical impurity measures. The new algorithm is called Polarized Classification Tree model. The model is tested on simulated and rea
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GUIDOTTI, DARIO. "Verification and Repair of Machine Learning Models." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1082694.

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In these last few years, machine learning (ML) has gained incredible traction in the Artificial Intelligence community, and ML models have found successful applications in many different domains across computer science. However, it is hard to provide any formal guarantee on the behavior of ML models, and therefore their reliability is still in doubt, especially concerning their deployment in safety and security-critical applications. Verification and repair emerged as promising solutions to address some of these problems. In this dissertation, we present our contributions to these two lines
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Pontes, Miranda James William. "Federation of heterogeneous models with machine learning-assisted model views." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2025. http://www.theses.fr/2025IMTA0454.

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L’Ingénierie Dirigée par les Modèles (IDM) promeut les modèles comme un élément clé pour répondre à la complexité croissante du cycle de vie des systèmes logiciel. L’ingénierie de systèmes avec l’IDM implique divers modèles représentant différentes aspects du système. Cette hétérogénéité nécessite des capacités de fédération de modèles pour intégrer des points de vue spécifiques à de multiples domaines. Les solutions de Vues sur les Modèles (Model Views) répondent à ce défi mais manquent encore de support à l’automatisation. Cette thèse explore l’intégration de l’Apprentissage Automatique (AA)
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Markou, Markos N. "Models of novelty detection based on machine learning." Thesis, University of Exeter, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426165.

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14

Shepherd, T. "Dynamical models and machine learning for supervised segmentation." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/18729/.

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This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are invest
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Liu, Xiaoyang. "Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93737.

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Machine learning methods are now extensively applied in various scientific research areas to make models. Unlike regular models, machine learning based models use a data-driven approach. Machine learning algorithms can learn knowledge that are hard to be recognized, from available data. The data-driven approaches enhance the role of algorithms and computers and then accelerate the computation using alternative views. In this thesis, we explore the possibility of applying machine learning models in the prediction of chromatographic retention behaviors. Chromatographic separation is a key techni
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Gosch, Aron. "Exploration of 5G Traffic Models using Machine Learning." Thesis, Linköpings universitet, Databas och informationsteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-168160.

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The Internet is a major communication tool that handles massive information exchanges, sees a rapidly increasing usage, and offers an increasingly wide variety of services.   In addition to these trends, the services themselves have highly varying quality of service (QoS), requirements and the network providers must take into account the frequent releases of new network standards like 5G. This has resulted in a significant need for new theoretical models that can capture different network traffic characteristics. Such models are important both for understanding the existing traffic in networks
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Awaysheh, Abdullah Mamdouh. "Data Standardization and Machine Learning Models for Histopathology." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/85040.

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Machine learning can provide insight and support for a variety of decisions. In some areas of medicine, decision-support models are capable of assisting healthcare practitioners in making accurate diagnoses. In this work we explored the application of these techniques to distinguish between two diseases in veterinary medicine; inflammatory bowel disease (IBD) and alimentary lymphoma (ALA). Both disorders are common gastrointestinal (GI) diseases in humans and animals that share very similar clinical and pathological outcomes. Because of these similarities, distinguishing between these two dis
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Aryasomayajula, Naga Srinivasa Baradwaj. "Machine Learning Models for Categorizing Privacy Policy Text." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535633397362514.

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19

IGUIDER, WALID. "Machine Learning Models for Sports Remote Coaching Platforms." Doctoral thesis, Università degli Studi di Cagliari, 2022. http://hdl.handle.net/11584/326530.

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Offering timely support to users in eCoaching systems is a crucial factor to keep them engaged. However, coaches usually follow many users, so it is hard to prioritize those they should interact with first. Timeliness is especially needed when health implications might be the consequence of a lack of support. Thanks to the data provided by U4FIT (an eCoaching platform for runners we will describe in Chapter 1) and the rise of high-performance computing, Artificial Intelligence can turn such challenges into unparalleled opportunities. One of its sub-fields, namely Machine Learning, enables mach
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Goodman, Genghis. "A Machine Learning Approach to Artificial Floorplan Generation." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/89.

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The process of designing a floorplan is highly iterative and requires extensive human labor. Currently, there are a number of computer programs that aid humans in floorplan design. These programs, however, are limited in their inability to fully automate the creative process. Such automation would allow a professional to quickly generate many possible floorplan solutions, greatly expediting the process. However, automating this creative process is very difficult because of the many implicit and explicit rules a model must learn in order create viable floorplans. In this paper, we propose a met
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Romano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.

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In questo lavoro di tesi è stato analizzato l'avvento dell'industria 4.0 all'interno dell' industria nel settore packaging. In particolare, è stata discussa l'importanza della diagnostica predittiva e sono stati analizzati e testati diversi approcci per la determinazione di modelli descrittivi del problema a partire dai dati. Inoltre, sono state applicate le principali tecniche di Machine Learning in modo da classificare i dati analizzati nelle varie classi di appartenenza.
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Bhat, Sooraj. "Syntactic foundations for machine learning." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47700.

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Machine learning has risen in importance across science, engineering, and business in recent years. Domain experts have begun to understand how their data analysis problems can be solved in a principled and efficient manner using methods from machine learning, with its simultaneous focus on statistical and computational concerns. Moreover, the data in many of these application domains has exploded in availability and scale, further underscoring the need for algorithms which find patterns and trends quickly and correctly. However, most people actually analyzing data today operate far from the e
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Rado, Omesaad A. M. "Contributions to evaluation of machine learning models. Applicability domain of classification models." Thesis, University of Bradford, 2019. http://hdl.handle.net/10454/18447.

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Artificial intelligence (AI) and machine learning (ML) present some application opportunities and challenges that can be framed as learning problems. The performance of machine learning models depends on algorithms and the data. Moreover, learning algorithms create a model of reality through learning and testing with data processes, and their performance shows an agreement degree of their assumed model with reality. ML algorithms have been successfully used in numerous classification problems. With the developing popularity of using ML models for many purposes in different domains,
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Vantzelfde, Nathan Hans. "Prognostic models for mesothelioma : variable selection and machine learning." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33370.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.<br>Includes bibliographical references (leaves 103-107).<br>Malignant pleural mesothelioma is a rare and lethal form of cancer affecting the external lining of the lungs. Extrapleural pneumonectomy (EPP), which involves the removal of the affected lung, is one of the few treatments that has been shown to have some effectiveness in treatment of the disease [39], but this procedure carries with it a high risk of mortality and morbidity [8]. This paper is concerned with building mo
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Ebbesson, Markus. "Mail Volume Forecasting an Evaluation of Machine Learning Models." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-301333.

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This study applies machine learning models to mail volumes with the goal of making sufficiently accurate forecasts to minimise the problem of under- and overstaffing at a mail operating company. A most suitable model appraisal in the context is found by evaluating input features and three different models, Auto Regression (AR), Random Forest (RF) and Neural Network (NN) (Multilayer Perceptron (MLP)). The results provide exceedingly improved forecasting accuracies compared to the model that is currently in use. The RF model is recommended as the most practically applicable model for the company
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Wissel, Benjamin D. "Generalizability of Electronic Health Record-Based Machine Learning Models." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627659161796896.

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Pirgul, Khalid, та Jonathan Svensson. "Verification of Powertrain Simulation Models Using Machine Learning Methods". Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166290.

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This thesis is providing an insight into the verification of a quasi-static simulation model based on the estimation of fuel consumption using machine learning methods. Traditional verification using real test data is not always available. Therefore, a methodology consisting of verification analysis based on estimation methods was developed together with an improving process of a quasi-static simulation model. The modelling of the simulation model mainly consists of designing and implementing a gear selection strategy together with the gearbox itself for a dual clutch transmission dedicated to
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Elf, Sebastian, and Christopher Öqvist. "Comparison of supervised machine learning models forpredicting TV-ratings." Thesis, KTH, Hälsoinformatik och logistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278054.

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Abstract Manual prediction of TV-ratings to use for program and advertisement placement can be costly if they are wrong, as well as time-consuming. This thesis evaluates different supervised machine learning models to see if the process of predicting TV-ratings can be automated with better accuracy than the manual process. The results show that of the two tested supervised machine learning models, Random Forest and Support Vector Regression, Random Forest was the better model. Random Forest was better on both measurements, mean absolute error and root mean squared error, used to compare the mo
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Lanka, Venkata Raghava Ravi Teja Lanka. "VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084.

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Hugo, Linsey Sledge. "A Comparison of Machine Learning Models Predicting Student Employment." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1544127100472053.

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31

Snelson, Edward Lloyd. "Flexible and efficient Gaussian process models for machine learning." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1445855/.

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Gaussian process (GP) models are widely used to perform Bayesian nonlinear regression and classification tasks that are central to many machine learning problems. A GP is nonparametric, meaning that the complexity of the model grows as more data points are received. Another attractive feature is the behaviour of the error bars. They naturally grow in regions away from training data where we have high uncertainty about the interpolating function. In their standard form GPs have several limitations, which can be divided into two broad categories: computational difficulties for large data sets, a
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Zeng, Haoyang Ph D. Massachusetts Institute of Technology. "Machine learning models for functional genomics and therapeutic design." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122689.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 213-230).<br>Due to the limited size of training data available, machine learning models for biology have remained rudimentary and inaccurate despite the significant advance in machine learning research. With the recent advent
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Olubeko, Olasubomi O. "Machine learning models for screening and diagnosis of infections." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123039.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 71-74).<br>Millions of people around the globe die or are severely burdened every year at the hands of infections. These infections can occur in wounds on the surface of the body, often after surgery. They also occur inside th
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Macis, Ambra. "Statistical Models and Machine Learning for Survival Data Analysis." Doctoral thesis, Università degli studi di Brescia, 2023. https://hdl.handle.net/11379/568945.

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L'argomento principale di questa tesi è l'analisi della sopravvivenza, un insieme di metodi utilizzati negli studi longitudinali in cui l'interesse non è solo nel verificarsi (o meno) di un particolare evento, ma anche nel tempo necessario per osservarlo. Negli anni sono stati inizialmente proposti dei modelli statistici e, in seguito, sono stati introdotti anche metodi di machine learning per affrontare studi di analisi di sopravvivenza. La prima parte del lavoro fornisce un'introduzione ai concetti di base dell'analisi di sopravvivenza e un'ampia rassegna della letteratura esistente. Nello
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MARZIALI, ANDREA. "Machine learning models applied to energy time-series forecasting." Doctoral thesis, Università degli studi di Pavia, 2020. http://hdl.handle.net/11571/1326207.

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BARDELLI, CHIARA. "Machine Learning and Statistical models in real world applications." Doctoral thesis, Università degli studi di Pavia, 2021. http://hdl.handle.net/11571/1447635.

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Machine Learning and Statistical models are nowadays widely used in different fields of application thanks to their flexibility and adaptation to specific type of data and problem domain. Each part of this thesis presents a domain of application and proposes a specific approach to solve the problem described. A Bayesian framework is applied in the First and Third part of this thesis, while a Machine Learning approach is preferred in the Second part. The first part of this thesis proposes an algorithm for a new ensemble decision tree procedure based on Proper Bayesian bootstrap. The introductio
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Darwaish, Asim. "Adversary-aware machine learning models for malware detection systems." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7283.

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La popularisation des smartphones et leur caractère indispensable les rendent aujourd'hui indéniables. Leur croissance exponentielle est également à l'origine de l'apparition de nombreux logiciels malveillants et fait trembler le prospère écosystème mobile. Parmi tous les systèmes d'exploitation des smartphones, Android est le plus ciblé par les auteurs de logiciels malveillants en raison de sa popularité, de sa disponibilité en tant que logiciel libre, et de sa capacité intrinsèque à accéder aux ressources internes. Les approches basées sur l'apprentissage automatique ont été déployées avec s
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Mariet, Zelda Elaine. "Learning with generalized negative dependence : probabilistic models of diversity for machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122739.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 139-150).<br>This thesis establishes negative dependence as a powerful and computationally efficient framework to analyze machine learning problems that require a theoretical model of diversification. Examples of such problems include experimental design and model compression: subset-selection problems that require carefully balancing the quality of each selected element with the diversity o
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Cahill, Jaspar. "Machine learning techniques to improve software quality." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/41730/1/Jaspar_Cahill_Thesis.pdf.

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A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are
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Lundström, Love, and Oscar Öhman. "Machine Learning in credit risk : Evaluation of supervised machine learning models predicting credit risk in the financial sector." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-164101.

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When banks lend money to another party they face a risk that the borrower will not fulfill its obligation towards the bank. This risk is called credit risk and it’s the largest risk banks faces. According to the Basel accord banks need to have a certain amount of capital requirements to protect themselves towards future financial crisis. This amount is calculated for each loan with an attached risk-weighted asset, RWA. The main parameters in RWA is probability of default and loss given default. Banks are today allowed to use their own internal models to calculate these parameters. Thus hold ca
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Tahkola, M. (Mikko). "Developing dynamic machine learning surrogate models of physics-based industrial process simulation models." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906042313.

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Abstract. Dynamic physics-based models of industrial processes can be computationally heavy which prevents using them in some applications, e.g. in process operator training. Suitability of machine learning in creating surrogate models of a physics-based unit operation models was studied in this research. The main motivation for this was to find out if machine learning model can be accurate enough to replace the corresponding physics-based components in dynamic modelling and simulation software Apros® which is developed by VTT Technical Research Centre of Finland Ltd and Fortum. This study is
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MAURI, LARA. "DATA PARTITIONING AND COMPENSATION TECHNIQUES FOR SECURE TRAINING OF MACHINE LEARNING MODELS." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/932387.

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Advances in Machine Learning (ML), coupled with increased availability of huge amounts of data collected from diverse sources and improvements in computing power, have led to a widespread adoption of ML-based solutions in critical application scenarios. However, ML models intrinsically introduce new security vulnerabilities within the systems into which they are integrated, thereby expanding their attack surface. The security of ML-based systems hinges on the robustness of the ML model employed. By interfering with any of the phases of the learning process, an adversary can manipulate data and
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Abdullah, Siti Norbaiti binti. "Machine learning approach for crude oil price prediction." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/machine-learning-approach-for-crude-oil-price-prediction(949fa2d5-1a4d-416a-8e7c-dd66da95398e).html.

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Crude oil prices impact the world economy and are thus of interest to economic experts and politicians. Oil price’s volatile behaviour, which has moulded today’s world economy, society and politics, has motivated and continues to excite researchers for further study. This volatile behaviour is predicted to prompt more new and interesting research challenges. In the present research, machine learning and computational intelligence utilising historical quantitative data, with the linguistic element of online news services, are used to predict crude oil prices via five different models: (1) the H
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Garcia, Gomez David. "Exploration of customer churn routes using machine learning probabilistic models." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/144660.

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The ongoing processes of globalization and deregulation are changing the competitive framework in the majority of economic sectors. The appearance of new competitors and technologies entails a sharp increase in competition and a growing preoccupation among service providing companies with creating stronger bonds with customers. Many of these companies are shifting resources away from the goal of capturing new customers and are instead focusing on retaining existing ones. In this context, anticipating the customer¿s intention to abandon, a phenomenon also known as churn, and facilitating the la
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Rosenbaum, Lars [Verfasser]. "Interpretable Machine Learning Models for Mining Chemical Databases / Lars Rosenbaum." München : Verlag Dr. Hut, 2014. http://d-nb.info/1047036266/34.

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Rowland, Mark. "Structure in machine learning : graphical models and Monte Carlo methods." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/287479.

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This thesis is concerned with two main areas: approximate inference in discrete graphical models, and random embeddings for dimensionality reduction and approximate inference in kernel methods. Approximate inference is a fundamental problem in machine learning and statistics, with strong connections to other domains such as theoretical computer science. At the same time, there has often been a gap between the success of many algorithms in this area in practice, and what can be explained by theory; thus, an important research effort is to bridge this gap. Random embeddings for dimensionality re
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Adams, William A. "Analysis of Robustness in Lane Detection using Machine Learning Models." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449167611.

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Ida, Yasutoshi. "Algorithms for Accelerating Machine Learning with Wide and Deep Models." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263771.

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Fang, Yue. "Generalization of Electric Vehicles' Battery Cell Models with Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266108.

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Battery cells are the core of any electric vehicles (EV). It is important to get theaccurate value of different characteristics in a battery cell for safety and lifetime concern.And Two machine learning methods, support vector machine for regression(SVR) and multilayer perceptron (MLP) were introduced to improve the performanceof predicting the battery cell voltage using certain circuit related featureslike current, capacity and temperature on two different kinds of datasets. Differentconfigurations are validated using cross-validation to find the most suitable configurationof the parameters o
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Macali, Amalia. "Membrane Separation Processes Using Machine Learning Based Mathematical Programming Models." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0038.

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Le sujet principal de mon doctorat est l'optimisation de la technologie de séparation membranaire. La technologie de séparation par membrane est souvent utilisée pour purifier les gaz et peut être utilisée dans différents aspects de l'industrie. Les performances de la séparation membranaire dépendent des conditions de fonctionnement et des interconnexions entre les équipements sélectionnés. Les membranes pour la séparation des gaz peuvent être fabriquées à partir de différents matériaux, et chaque matériau entraîne des performances de perméabilité différentes. Lorsqu'un mélange de gaz pénètre
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