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Relevant bibliographies by topics / Methods of machine learning / Dissertations / Theses

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Dissertations / Theses on the topic 'Methods of machine learning'

Author: Grafiati

Published: 28 May 2022

Last updated: 18 July 2025

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1

Mauricio, Palacio Sebastián. "Machine-Learning Applied Methods." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/669286.

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The presented discourse followed several topics where every new chapter introduced an economic prediction problem and showed how traditional approaches can be complemented with new techniques like machine learning and deep learning. These powerful tools combined with principles of economic theory is highly increasing the scope for empiricists. Chapter 3 addressed this discussion. By progressively moving from Ordinary Least Squares, Penalized Linear Regressions and Binary Trees to advanced ensemble trees. Results showed that ML algorithms significantly outperform statistical models in terms of

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VELLOSO, SUSANA ROSICH SOARES. "SQLLOMINING: FINDING LEARNING OBJECTS USING MACHINE LEARNING METHODS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10970@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Objetos de Aprendizagem ou Learning Objects (LOs) são porções de material didático tais como textos que podem ser reutilizados na composição de outros objetos maiores (aulas ou cursos). Um dos problemas da reutilização de LOs é descobri-los em seus contextos ou documentos texto originais tais como livros, e artigos. Visando a obtenção de LOs, este trabalho apresenta um processo que parte da extração, tratamento e carga de uma base de dados textual e em seguida, baseando-se em técnicas de aprendizado de máquina, uma combinaç

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Felldin, Markus. "Machine Learning Methods for Fault Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183132.

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This project, conducted at Ericsson AB, investigates the feasibility of implementing machine learning techniques in order to classify dump files for more effi cient trouble report routing. The project focuses on supervised machine learning methods and in particular Bayesian statistics. It shows that a program utilizing Bayesian methods can achieve well above random prediction accuracy. It is therefore concluded that machine learning methods may indeed become a viable alternative to human classification of trouble reports in the near future.<br>Detta examensarbete, utfört på Ericsson AB, ämnar

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Khan, Muhammad Naeem Ahmed. "Digital Forensics using Machine Learning Methods." Thesis, University of Sussex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487975.

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The increase in computer related crimes, with particular reference to internet crimes, has led to an increasing demand for state-of-the-art digital forensics. Reconstruction of the past events in chronological order is crucial for digital forensic investigations to pinpoint the execution of relevant application programs and the files manipulated by those applications. The event reconstruction process can be made more objective and rigorous by employing mathematical techniques due to their sound theoretical foundations. The focus of this research is to explore the effectiveness of employing mac

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Li, Limin, and 李丽敏. "Machine learning methods for computational biology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44546749.

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Chlon, Leon. "Machine learning methods for cancer immunology." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/268068.

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Tumours are highly heterogeneous collections of tissues characterised by a repertoire of heavily mutated and rapidly proliferating cells. Evading immune destruction is a fundamental hallmark of cancer, and elucidating the contextual basis of tumour-infiltrating leukocytes is pivotal for improving immunotherapy initiatives. However, progress in this domain is hindered by an incomplete characterisation of the regulatory mechanisms involved in cancer immunity. Addressing this challenge, this thesis is formulated around a fundamental line of inquiry: how do we quantitatively describe the immune sy

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Chang, Allison An. "Integer optimization methods for machine learning." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72643.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 129-137).<br>In this thesis, we propose new mixed integer optimization (MIO) methods to ad- dress problems in machine learning. The first part develops methods for supervised bipartite ranking, which arises in prioritization tasks

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Lowe, Robert Alexander. "Investigating machine learning methods in chemistry." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610567.

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Berry, Jeffrey James. "Machine Learning Methods for Articulatory Data." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/223348.

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Humans make use of more than just the audio signal to perceive speech. Behavioral and neurological research has shown that a person's knowledge of how speech is produced influences what is perceived. With methods for collecting articulatory data becoming more ubiquitous, methods for extracting useful information are needed to make this data useful to speech scientists, and for speech technology applications. This dissertation presents feature extraction methods for ultrasound images of the tongue and for data collected with an Electro-Magnetic Articulograph (EMA). The usefulness of these featu

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Marakani, Sumeesha. "Employee Matching Using Machine Learning Methods." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18493.

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Background: Expertise retrieval is an information retrieval technique that focuses on techniques to identify the most suitable ’expert’ for a task from a list of individuals. Objectives: This master thesis is a collaboration with Volvo Cars to attempt applying this concept and match employees based on information that was extracted from an internal tool of the company. In this tool, the employees describe themselves in free-flowing text. This text is extracted from the tool and analyzed using Natural Language Processing (NLP) techniques. Methods: Through the course of this project, various tec

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Barlacchi, Gianni. "Machine Learning Methods for Urban Computing." Doctoral thesis, Università degli studi di Trento, 2019. https://hdl.handle.net/11572/368726.

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Machine Learning Methods for Urban Computing World population is increasingly moving from rural areas to urban centers, making large cities densely populated. In urban areas, there is greater access to work, a wide variety of options for education and training, ease of transport and the abundance of attractive places within a few kilometers. Across huge cities, people tend to move more and have to do it faster than in the past. On the other hand, heavy traffic (e.g., traffic jams), overbuilding and changes in the urban lifestyle can cause several new problems such as noise, atmospheric polluti

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Barlacchi, Gianni. "Machine Learning Methods for Urban Computing." Doctoral thesis, University of Trento, 2019. http://eprints-phd.biblio.unitn.it/3592/1/DECLARATORIA_ENG_bruno_(1).pdf.

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Machine Learning Methods for Urban Computing World population is increasingly moving from rural areas to urban centers, making large cities densely populated. In urban areas, there is greater access to work, a wide variety of options for education and training, ease of transport and the abundance of attractive places within a few kilometers. Across huge cities, people tend to move more and have to do it faster than in the past. On the other hand, heavy traffic (e.g., traffic jams), overbuilding and changes in the urban lifestyle can cause several new problems such as noise, atmospheric polluti

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Verleyen, Wim. "Machine learning for systems pathology." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4512.

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Systems pathology attempts to introduce more holistic approaches towards pathology and attempts to integrate clinicopathological information with “-omics” technology. This doctorate researches two examples of a systems approach for pathology: (1) a personalized patient output prediction for ovarian cancer and (2) an analytical approach differentiates between individual and collective tumour invasion. During the personalized patient output prediction for ovarian cancer study, clinicopathological measurements and proteomic biomarkers are analysed with a set of newly engineered bioinformatic tool

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Arslan, Hilal. "Machine Learning Methods For Promoter Region Prediction." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613363/index.pdf.

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Promoter classification is the task of separating promoter from non promoter sequences. Determining promoter regions where the transcription initiation takes place is important for several reasons such as improving genome annotation and defining transcription start sites. In this study, various promoter prediction methods called ProK-means, ProSVM, and 3S1C are proposed. In ProSVM and ProK-means algorithms, structural features of DNA sequences are used to distinguish promoters from non promoters. Obtained results are compared with ProSOM which is an existing promoter prediction method. It is s

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Peng, Huiping. "Air quality prediction by machine learning methods." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/55069.

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As air pollution is a complex mixture of toxic components with considerable impact on humans, forecasting air pollution concentration emerges as a priority for improving life quality. In this study, air quality data (observational and numerical) were used to produce hourly spot concentration forecasts of ozone (O₃), particulate matter 2.5μm (PM₂.₅) and nitrogen dioxide (NO₂), up to 48 hours for six stations across Canada -- Vancouver, Edmonton, Winnipeg, Toronto, Montreal and Halifax. Using numerical data from an air quality model (GEM-MACH15) as predictors, forecast models for pollutant conce

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Qin, Lei. "Online machine learning methods for visual tracking." Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0017/document.

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Nous étudions le problème de suivi de cible dans une séquence vidéo sans aucune connaissance préalable autre qu'une référence annotée dans la première image. Pour résoudre ce problème, nous proposons une nouvelle méthode de suivi temps-réel se basant sur à la fois une représentation originale de l’objet à suivre (descripteur) et sur un algorithme adaptatif capable de suivre la cible même dans les conditions les plus difficiles comme le cas où la cible disparaît et réapparait dans le scène (ré-identification). Tout d'abord, pour la représentation d’une région de l’image à suivre dans le temps,

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Reddi, Sashank Jakkam. "New Optimization Methods for Modern Machine Learning." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1116.

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Modern machine learning systems pose several new statistical, scalability, privacy and ethical challenges. With the advent of massive datasets and increasingly complex tasks, scalability has especially become a critical issue in these systems. In this thesis, we focus on fundamental challenges related to scalability, such as computational and communication efficiency, in modern machine learning applications. The underlying central message of this thesis is that classical statistical thinking leads to highly effective optimization methods for modern big data applications. The first part of the

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Havaei, Seyed Mohammad. "Machine learning methods for brain tumor segmentation." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10260.

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Abstract : Malignant brain tumors are the second leading cause of cancer related deaths in children under 20. There are nearly 700,000 people in the U.S. living with a brain tumor and 17,000 people are likely to loose their lives due to primary malignant and central nervous system brain tumor every year. To identify whether a patient is diagnosed with brain tumor in a non-invasive way, an MRI scan of the brain is acquired followed by a manual examination of the scan by an expert who looks for lesions (i.e. cluster of cells which deviate from healthy tissue). For treatment purposes, the tumor

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Hussain, Sibt Ul. "Machine Learning Methods for Visual Object Detection." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00680048.

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The goal of this thesis is to develop better practical methods for detecting common object classes in real world images. We present a family of object detectors that combine Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) features with efficient Latent SVM classifiers and effective dimensionality reduction and sparsification schemes to give state-of-the-art performance on several important datasets including PASCAL VOC2006 and VOC2007, INRIA Person and ETHZ. The three main contributions are as follows. Firstly, we pioneer the use of Local Ternar

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Michailidis, Marios. "Investigating machine learning methods in recommender systems." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/10031000/.

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This thesis investigates the use of machine learning in improving predictions of the top K* product purchases at a particular a retailer. The data used for this research is a freely-available (for research) sample of the retailer’s transactional data spanning a period of 102 weeks and consisting of several million observations. The thesis consists of four key experiments: 1. Univariate Analysis of the Dataset: The first experiment, which is the univariate analysis of the dataset, sets the background to the following chapters. It provides explanatory insight into the customers’ shopping behavio

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Higson, Edward John. "Bayesian methods and machine learning in astrophysics." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289728.

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This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a popular method for Bayesian computation which is widely used in astrophysics. Following the introduction and background material in Chapters 1 and 2, Chapter 3 analyses the sampling errors in nested sampling parameter estimation and presents a method for estimating them numerically for a single nested sam

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Raina, Kevin. "Machine Learning Methods for Brain Lesion Delineation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41156.

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Brain lesions are regions of abnormal or damaged tissue in the brain, commonly due to stroke, cancer or other disease. They are diagnosed primarily using neuroimaging, the most common modalities being Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Brain lesions have a high degree of variability in terms of location, size, intensity and form, which makes diagnosis challenging. Traditionally, radiologists diagnose lesions by inspecting neuroimages directly by eye; however, this is time-consuming and subjective. For these reasons, many automated methods have been developed fo

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Hussain, Sabit ul. "Machine Learning Methods for Visual Object Detection." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENM070/document.

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Le but de cette thèse est de développer des méthodes pratiques plus performantes pour la détection d'instances de classes d'objets de la vie quotidienne dans les images. Nous présentons une famille de détecteurs qui incorporent trois types d'indices visuelles performantes – histogrammes de gradients orientés (Histograms of Oriented Gradients, HOG), motifs locaux binaires (Local Binary Patterns, LBP) et motifs locaux ternaires (Local Ternary Patterns, LTP) – dans des méthodes de discrimination efficaces de type machine à vecteur de support latent (Latent SVM), sous deux régimes de réduction de

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Gabbur, Prasad. "Machine Learning Methods for Microarray Data Analysis." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/195829.

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Microarrays emerged in the 1990s as a consequence of the efforts to speed up the process of drug discovery. They revolutionized molecular biological research by enabling monitoring of thousands of genes together. Typical microarray experiments measure the expression levels of a large numberof genes on very few tissue samples. The resulting sparsity of data presents major challenges to statistical methods used to perform any kind of analysis on this data. This research posits that phenotypic classification and prediction serve as good objective functions for both optimization and evaluation of

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Engström, Isak, and Alan Ihre. "Predicting house prices with machine learning methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260140.

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In this study, the machine learning algorithms k-Nearest-Neighbours regression (k-NN) and Random Forest (RF) regression were used to predict house prices from a set of features in the Ames housing data set. The algorithms were selected from an assessment of previous research and the intent was to compare their relative performance at this task. Software implementations for the experiment were selected from the scikit-learn Python library and executed to calculate the error between the actual and predicted sales price using four different metrics. Hyperparameters for the algorithms used were op

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Darwiche, Aiman A. "Machine Learning Methods for Septic Shock Prediction." Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1051.

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Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify

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Fallah, Alireza. "Robust accelerated gradient methods for machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122881.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 91-95).<br>In this thesis, we study the problem of minimizing a smooth and strongly convex function, which arises in different areas, including regularized regression problems in machine learning. To solve this optimization problem, we consider using first order methods which are popular due to their scalability with large data sets, and we study the case that the exact gradient information is

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Casavant, Matt(Matt Stephen). "Predicting competitor restructuring using machine learning methods." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122595.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 59-60).<br>Increasing competition in the defense industry risks contract margin degradation and increases the need for new avenues to margin expansion. One such area of opportunity is ta

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Rampini, Francesco <1995&gt. "Performance measures forecasting applying Machine Learning methods." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/21339.

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The goal of this thesis is to improve performance management and business choices applying Machine Learning (ML) methods, exploiting the opportunities offered by Big Data. In fact, Big Data with the support of Business Intelligence (BI) and Business Analytics (BA) may be utilized to analyze large volumes of data in order to build machine learning algorithms to assist organizations to make better and faster decisions. In particular, building a forecasting model for a KPI or for other performance measures is useful because helps to plan and manage the business, to discover the influence exercisa

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Brandão, Eduardo. "Complexity Methods in Physics-Guided Machine Learning." Electronic Thesis or Diss., Saint-Etienne, 2023. http://www.theses.fr/2023STET0062.

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La complexité est facile à reconnaître mais difficile à définir : il existe de nombreuses mesures de complexité, chacune pertinente pour une application particulière.Dans le domaine de l'ingénierie des surfaces, l'auto-organisation entraîne la formation de motifs sur la matière par irradiation laser femtoseconde, ce qui a d'importantes applications biomédicales. Les détails de la formation des motifs ne sont pas entièrement compris. Dans des travaux menant à deux publications [1,2], grâce à un argument de complexité et un cadre d'apprentissage automatique guidé par la physique, nous montrons q

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Tsang, Wai-Hung. "Kernel methods in supervised and unsupervised learning /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20TSANG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 46-49). Also available in electronic version. Access restricted to campus users.

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SOAVE, Elia. "Diagnostics and prognostics of rotating machines through cyclostationary methods and machine learning." Doctoral thesis, Università degli studi di Ferrara, 2022. http://hdl.handle.net/11392/2490999.

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In the last decades, the vibration analysis has been exploited for monitoring many mechanical systems for industrial applications. Although several works demonstrated how the vibration based diagnostics may reach satisfactory results, the nowadays industrial scenario is deeply changing, driven by the fundamental need of time and cost reduction. In this direction, the academic research has to focus on the improvement of the computational efficiency for the signal processing techniques applied in the mechanical diagnostics field. In the same way, the industrial word requires an increasing attent

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Berrar, Daniel. "Machine learning methods for analyzing DNA microarray data." Thesis, University of Ulster, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414098.

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Haghiri, Siavash [Verfasser]. "Comparison-based methods in machine learning / Siavash Haghiri." Tübingen : Universitätsbibliothek Tübingen, 2021. http://d-nb.info/1233678515/34.

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Dias, Pedro Ricardo Gomes. "Recommending media content based on machine learning methods." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/6581.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>Information is nowadays made available and consumed faster than ever before. This information technology generation has access to a tremendous deal of data and is left with the heavy burden of choosing what is relevant. With the increasing growth of media sources, the amount of content made available to users has become overwhelming and in need to be managed. Recommender systems emerged with the purpose of providing personalized and meaningful content recommendations based on users’ preferences and usage history. Due

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Isaksson, Ola. "Classification of Flying Qualities with Machine Learning Methods." Thesis, KTH, Flygdynamik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302145.

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The primary objective of this thesis is to evaluate the prospect of machine learning methods being used to classify flying qualities based on simulator data (with the focus being on pitch maneuvers). If critical flying qualities could be identified earlier in the verification process, they can be further invested in and focused on with less cost for design changes of the flight control system. Information from manned simulations with given flying quality levels are used to create a replication of the performed pitch maneuver in a desktop simulator. The generated flight data is represented by d

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Feng, Zijie. "Machine learning methods for seasonal allergic rhinitis studies." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173090.

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Seasonal allergic rhinitis (SAR) is a disease caused by allergens from both environmental and genetic factors. Some researchers have studied the SAR based on traditional genetic methodologies. As technology develops, a new technique called single-cell RNA sequencing (scRNA-seq) is developed, which can generate high-dimension data. We apply two machine learning (ML) algorithms, random forest (RF) and partial least squares discriminant analysis (PLS-DA), for cell source classification and gene selection based on the SAR scRNA-seq time-series data from three allergic patients and four healthy con

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Bodin, Camilla. "Automatic Flight Maneuver Identification Using Machine Learning Methods." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165844.

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This thesis proposes a general approach to solve the offline flight-maneuver identification problem using machine learning methods. The purpose of the study was to provide means for the aircraft professionals at the flight test and verification department of Saab Aeronautics to automate the procedure of analyzing flight test data. The suggested approach succeeded in generating binary classifiers and multiclass classifiers that identified six flight maneuvers of different complexity from real flight test data. The binary classifiers solved the problem of identifying one maneuver from flight tes

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Höglund, Melker. "Machine Learning Methods for Spatial Interpolation of Wind." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275743.

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In this study, two popular machine learning approaches and a number of common simple spatial interpolation techniques are applied to spatial estimation of wind field observations in Sweden. Specifically, neural network and random forest models using geographical coordinates as input variables are considered. Furthermore, the addition of elevation as a secondary input is studied. The accuracy of the methods is assessed using a leave-one-out cross-validation scheme. Visual examination of the resulting interpolation fields and interpolation errors is used as an additional point of comparison. The

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Rowan, Adriaan. "Unravelling black box machine learning methods using biplots." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31124.

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Following the development of new mathematical techniques, the improvement of computer processing power and the increased availability of possible explanatory variables, the financial services industry is moving toward the use of new machine learning methods, such as neural networks, and away from older methods such as generalised linear models. However, their use is currently limited because they are seen as “black box” models, which gives predictions without justifications and which are therefore not understood and cannot be trusted. The goal of this dissertation is to expand on the theory an

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Paes, Herrera Gabriel. "Implementation of Machine Learning Methods in Applied Economics." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/417697.

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This thesis aims to expand the research frontier and contribute to the literature on the application of machine learning (ML) in economics. Traditional econometric methods and machine learning modes have their advantages and disadvantages. Analysis techniques are constantly evolving to provide better solutions to solve problems and extract insights from data. This work includes four chapters, each of which explores a relevant application of ML algorithms in different economic segments. Apart from that, the thesis also includes an introduction and a final chapter containing concluding remarks.

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Caccin, Marco. "Oxide chemomechanics by hybrid atomistic machine learning methods." Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/oxide-chemomechanics-by-hybrid-atomistic-machine-learning-methods(e69ed02a-5b80-4353-aa55-e5186e8cc36f).html.

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Atomic scale phenomena concurring to atomic bond ruptures at a crack tip determine the chemomechanical properties of oxide materials, thus a better understanding of them is instrumental in addressing engineering issues related to the brittleness of oxides. In a fracturing material, the macroscopic stress field couples with the chemical reactions occurring at the crack tip in a bidirectional interplay requiring a concurrent multiscale (QM/MM) computational approach. Due to long range electrostatic interactions, the dynamics of chemically accurate description of the neighbourhood of a breaking b

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Michael, Christoph Cornelius. "General methods for analyzing machine learning sample complexity." W&M ScholarWorks, 1994. https://scholarworks.wm.edu/etd/1539623860.

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During the past decade, there has been a resurgence of interest in applying mathematical methods to problems in artificial intelligence. Much work has been done in the field of machine learning, but it is not always clear how the results of this research should be applied to practical problems. Our aim is to help bridge the gap between theory and practice by addressing the question: "If we are given a machine learning algorithm, how should we go about formally analyzing it?" as opposed to the usual question: "how do we write a learning algorithm we can analyze?".;We will consider algorithms th

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Ivarsson, Anton, and Jacob Stachowicz. "Evaluating machine learning methods for detecting sleep arousal." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259996.

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Sleep arousal is a phenomenon that affects the sleep of a large amount of people. The process of predicting and classifying arousal events is done manually with the aid of certified technologists, although some research has been done on automation using Artificial Neural Networks (ANN). This study explored how a Support Vector Machine performed(SVM) compared to an ANN on this task. Polysomnography (PSG) is a sort of sleep study which produces the data that is used in classifying sleep disorders. The PSG-data used in this thesis consists of 13 wave forms sampled at or resampled at 200Hz. There

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Zeng, Yaohui. "Scalable sparse machine learning methods for big data." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/6021.

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Sparse machine learning models have become increasingly popular in analyzing high-dimensional data. With the evolving era of Big Data, ultrahigh-dimensional, large-scale data sets are constantly collected in many areas such as genetics, genomics, biomedical imaging, social media analysis, and high-frequency finance. Mining valuable information efficiently from these massive data sets requires not only novel statistical models but also advanced computational techniques. This thesis focuses on the development of scalable sparse machine learning methods to facilitate Big Data analytics. Built upo

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Masti, Daniele. "Machine learning methods for control, identification, and estimation." Thesis, IMT Alti Studi Lucca, 2021. http://e-theses.imtlucca.it/342/1/Masti_phdthesis.pdf.

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Over the last decades, the landscape of control theory and system identification has changed significantly in response to the new challenges arising from the industry. This is not surprising: classical model-based techniques are not suitable to handle real-world applications for which it is often too expensive to derive even an approximate model using first principles. Data-driven approaches represent a solution to such an issue. Thanks to the ever-increasing availability of a large quantity of data, they have quickly become central topics within the control theory community. This th

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Detassis, Fabrizio <1991&gt. "Methods for integrating machine learning and constrained optimization." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10360/1/Detassis_Fabrizio_Thesis_Final.pdf.

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In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve th

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COLETTA, GIORGIA. "Monitoring of architectural heritage with machine learning methods." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2971117.

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DI, GIACOMO UMBERTO ANTONIO. "Machine learning and formal methods for sport analytics." Doctoral thesis, Università degli studi del Molise, 2022. https://hdl.handle.net/11695/115353.

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L’obiettivo principale del mio periodo di Dottorato riguarda il concetto della Soccer Analytics. Durante gli ultimi anni la Soccer Analytics ha registrato un’enorme diffusione. Di solito, in quest’ambito, i ricercatori si sono concentrati sulla predizione dei risultati delle partite anche se, soprattutto negli ultimi tempi, c’è stata un’attenzione particolare nello studio dei dati posizionali. Il mio periodo di Dottorato può essere diviso in tre parti fondamentali: nella prima parte ho preso in considerazione l’analisi e lo studio delle attività svolte dall’uomo; nella seconda parte, mi sono

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Bayerlein, Harald. "Machine Learning Methods for UAV-aided Wireless Networks." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS154.

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Les drones autonomes sont envisagés pour une multitude d'applications au service de la société du futur. Du point de vue des réseaux sans-fil de la prochaine génération, les drones ne sont pas seulement prévus dans le rôle d'utilisateurs passifs connectés au réseau cellulaire, mais aussi comme facilitateurs actifs de la connectivité dans le cadre de réseaux assistés par drones. L'avantage déterminant des drones dans tous les scénarios d'application potentiels est leur mobilité. Pour tirer pleinement parti de leurs capacités, des méthodes de planification de trajectoire flexibles et efficaces s

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