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1

Ford, John M. "Pulsar Search Using Supervised Machine Learning." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1001.

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Pulsars are rapidly rotating neutron stars which emit a strong beam of energy through mechanisms that are not entirely clear to physicists. These very dense stars are used by astrophysicists to study many basic physical phenomena, such as the behavior of plasmas in extremely dense environments, behavior of pulsar-black hole pairs, and tests of general relativity. Many of these tasks require information to answer the scientific questions posed by physicists. In order to provide more pulsars to study, there are several large-scale pulsar surveys underway, which are generating a huge backlog of u
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2

Ulrich, Jan. "Supervised machine learning for email thread summarization." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2363.

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Email has become a part of most people's lives, and the ever increasing amount of messages people receive can lead to email overload. We attempt to mitigate this problem using email thread summarization. Summaries can be used for things other than just replacing an incoming email message. They can be used in the business world as a form of corporate memory, or to allow a new team member an easy way to catch up on an ongoing conversation. Email threads are of particular interest to summarization because they contain much structural redundancy due to their conversational nature. Our email threa
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Raisi, Elaheh. "Weakly Supervised Machine Learning for Cyberbullying Detection." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/89100.

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The advent of social media has revolutionized human communication, significantly improving individuals' lives. It makes people closer to each other, provides access to enormous real-time information, and eases marketing and business. Despite its uncountable benefits, however, we must consider some of its negative implications such as online harassment and cyberbullying. Cyberbullying is becoming a serious, large-scale problem damaging people's online lives. This phenomenon is creating a need for automated, data-driven techniques for analyzing and detecting such behaviors. In this research, we
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4

Pehrson, Jakob, and Sara Lindstrand. "Support Unit Classification through Supervised Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281537.

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The purpose of this article is to evaluate the impact a supervised machine learning classification model can have on the process of internal customer support within a large digitized company. Chatbots are becoming a frequently used utility among digital services, though the true general impact is not always clear. The research is separated into the following two questions: (1) Which supervised machine learning algorithm of naïve Bayes, logistic regression, and neural networks can best predict the correct support a user needs and with what accuracy? And (2) What is the effect on the productivit
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Rexby, Mattias. "SUPERVISED MACHINE LEARNING (SML) IN SIMULATED ENVIRONMENTS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54698.

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Artificial intelligence has made a big impact on the world in recent years, and more knowledge inthe subject seems to be of vital importance as the possibilities seems endless. Is it possible to teacha computer to drive a car in a virtual environment, by training a neural network to act intelligentlythrough the usage of supervised machine learning? With less than 2 hours of data collected whenpersonally driving the car, I show that yes, it is indeed possible. This is done by applying thetechniques of supervised machine learning combined in conjunction with a deep convolutional neuralnetwork. T
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Teatini, Alex. "Movement trajectory classification using supervised machine learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265009.

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Anything that moves can be tracked, and hence its trajectory analysed. The trajectory of a moving object can carry a lot of useful information depending on what is sought. In this work, the aim is to exploit machine learning to be able to classify finite trajectories based on their shape. In a clinical environment, a set of trajectory classes have been defined based on relevance to particular pathologies. Furthermore, several trajectories have been collected using a depth sensor from a number of subjects. The problem to address is to evaluate whether it is possible to classify these trajectori
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Chang, Hong. "Semi-supervised distance metric learning /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20CHANG.

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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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9

Merat, Sepehr. "Clustering Via Supervised Support Vector Machines." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/857.

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An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabeled data) then has its labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labeled data). The repetition
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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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Granström, Daria, and Johan Abrahamsson. "Loan Default Prediction using Supervised Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252312.

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It is essential for a bank to estimate the credit risk it carries and the magnitude of exposure it has in case of non-performing customers. Estimation of this kind of risk has been done by statistical methods through decades and with respect to recent development in the field of machine learning, there has been an interest in investigating if machine learning techniques can perform better quantification of the risk. The aim of this thesis is to examine which method from a chosen set of machine learning techniques exhibits the best performance in default prediction with regards to chosen model
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Bawaskar, Neerja Pramod. "Analog Implicit Functional Testing using Supervised Machine Learning." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/2099.

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Testing analog circuits is more difficult than digital circuits. The reasons for this difficulty include continuous time and amplitude signals, lack of well-accepted testing techniques and time and cost required for its realization. The traditional method for testing analog circuits involves measuring all the performance parameters and comparing the measured parameters with the limits of the data-sheet specifications. Because of the large number of data-sheet specifications, the test generation and application requires long test times and expensive test equipment. This thesis proposes an impli
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Sîrbu, Adela-Maria. "Dynamic machine learning for supervised and unsupervised classification." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.

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La direction de recherche que nous abordons dans la thèse est l'application des modèles dynamiques d'apprentissage automatique pour résoudre les problèmes de classification supervisée et non supervisée. Les problèmes particuliers que nous avons décidé d'aborder dans la thèse sont la reconnaissance des piétons (un problème de classification supervisée) et le groupement des données d'expression génétique (un problème de classification non supervisée). Les problèmes abordés sont représentatifs pour les deux principaux types de classification et sont très difficiles, ayant une grande importance da
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Kola, Lokesh, and Vigneshwar Muriki. "A Comparison on Supervised and Semi-Supervised Machine Learning Classifiers for Diabetes Prediction." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21816.

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Background: The main cause of diabetes is due to high sugar levels in the blood. There is no permanent cure for diabetes. However, it can be prevented by early diagnosis. In recent years, the hype for Machine Learning is increasing in disease prediction especially during COVID-19 times. In the present scenario, it is difficult for patients to visit doctors. A possible framework is provided using Machine Learning which can detect diabetes at early stages. Objectives: This thesis aims to identify the critical features that impact gestational (Type-3) diabetes and experiments are performed to ide
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Cangini, Nicolò. "Quantum Supervised Learning: Algoritmi e implementazione." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17694/.

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Il Quantum Computing non riguarda più soltanto la scienza della Fisica, negli ultimi anni infatti la ricerca in questo campo ha subito una notevole espansione dimostrando l'enorme potenziale di cui dispongono questi nuovi calcolatori che in un futuro prossimo potranno rivoluzionare il concetto di Computer Science così come lo conosciamo. Ad oggi, siamo già in grado di realizzare algoritmi su piccola scala eseguibili in un quantum device grazie ai quali è possibile sperimentare uno speed-up notevole (in alcuni casi esponenziale) su diversi task tipici della computazione classica. In questo elab
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Huss, Jakob. "Cross Site Product Page Classification with Supervised Machine Learning." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189555.

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This work outlines a possible technique for identifying webpages that contain product  specifications. Using support vector machines a product web page classifier was constructed and tested with various settings. The final result for this classifier ended up being 0.958 in precision and 0.796 in recall for product pages. The scores imply that the method could be considered a valid technique in real world web classification tasks if additional features and more data were made available.
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Taylor, Farrell R. "Evaluation of Supervised Machine Learning for Classifying Video Traffic." NSUWorks, 2016. http://nsuworks.nova.edu/gscis_etd/972.

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Operational deployment of machine learning based classifiers in real-world networks has become an important area of research to support automated real-time quality of service decisions by Internet service providers (ISPs) and more generally, network administrators. As the Internet has evolved, multimedia applications, such as voice over Internet protocol (VoIP), gaming, and video streaming, have become commonplace. These traffic types are sensitive to network perturbations, e.g. jitter and delay. Automated quality of service (QoS) capabilities offer a degree of relief by prioritizing network t
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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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Gentek, Anna. "Activity Recognition Using Supervised Machine Learning and GPS Sensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295600.

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Human Activity Recognition has become a popular research topic among data scientists. Over the years, multiple studies regarding humans and their daily motion habits have been investigated for many different purposes. This fact is not surprising when we look at all the opportunities and applications that can be applied and utilized thanks to the results of these algorithms. In this project we implement a system that can effectively collect sensor data from mobile devices, process it and by using supervised machine learning successfully predict the class of a performed activity. The project was
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Mann, Anna, and Olivia Höft. "Categorization of Swedish e-mails using Supervised Machine Learning." Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296558.

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Society today is becoming more digitalized, and a common way of communication is to send e-mails. Currently, the company Auranest has a filtering method for categorizing e-mails, but the method is a few years old. The filter provides a classification of valuable e-mails for jobseekers, where employers can make contact. The company wants to know if the categorization can be performed with a different method and improved. The degree project aims to investigate whether the categorization can be proceeded with higher accuracy using machine learning. Three supervised machine learning algorithms, Na
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Chencho. "Efficient Supervised Machine Learning Techniques for Structural Health Monitoring." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/89294.

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This thesis presents supervised machine learning techniques using acceleration responses recorded from a small number of sensors. Ensemble-based traditional machine learning models are developed as a multi output regression model for the damage identification of the civil engineering structures using acceleration responses and impulse response functions extracted from it. Further, to improve the damage identification performance, a LSTM auto-encoder based multi output regression model is proposed. Finally, for a large-scale bridge, a 1D-CNN based damage classifier is developed using less numbe
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VIZZARI, MARIA TERESA. "Supervised Machine Learning and ABC for population genetic inference." Doctoral thesis, Università degli studi di Ferrara, 2021. http://hdl.handle.net/11392/2478816.

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Questa tesi riassume il lavoro di ricerca da me svolto durante i tre anni del dottorato, che finora ha portato alla pubblicazione di due articoli su riviste scientifiche. Questi studi sono incentrati sullo sviluppo di un nuovo framework ABC, basato su un algoritmo di machine-learning chiamato Random Forest, che consenta l'analisi di dati genomici completi per indagare i processi evolutivi passati che caratterizzano le popolazioni naturali. L’inferenza delle dinamiche demografiche passate è cruciale negli studi di genetica delle popolazioni e la grande quantità di genomi completi ad oggi dispo
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Verger, Mélina. "Algorithmic fairness analyses of supervised machine learning in education." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS600.

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Cette thèse vise à évaluer et réduire l'iniquité algorithmique des modèles d'apprentissage automatique largement utilisés en éducation. En effet, ces modèles prédictifs, fondés sur des données éducatives et des traces d'apprentissage de plus en plus abondantes, ont pour but d'améliorer l'expérience d'apprentissage humain. Ils permettent, par exemple, de prédire le décrochage scolaire ou de personnaliser l'expérience d'apprentissage en adaptant les contenus éducatifs selon les besoins de chaque apprenant et apprenante. Cependant, il a été démontré à plusieurs reprises que ces modèles peuvent pr
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VANCE, DANNY W. "AN ALL-ATTRIBUTES APPROACH TO SUPERVISED LEARNING." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1162335608.

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Stanescu, Ana. "Semi-supervised learning for biological sequence classification." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/35810.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>Doina Caragea<br>Successful advances in biochemical technologies have led to inexpensive, time-efficient production of massive volumes of data, DNA and protein sequences. As a result, numerous computational methods for genome annotation have emerged, including machine learning and statistical analysis approaches that practically and efficiently analyze and interpret data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data in order to build quality classifiers. T
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Ruan, Yang. "Smooth and locally linear semi-supervised metric learning /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20RUAN.

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Falk, Anton, and Daniel Holmgren. "Sales Forecasting by Assembly of Multiple Machine Learning Methods : A stacking approach to supervised machine learning." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184317.

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Today, digitalization is a key factor for businesses to enhance growth and gain advantages and insight in their operations. Both in planning operations and understanding customers the digitalization processes today have key roles, and companies are spending more and more resources in this fields to gain critical insights and enhance growth. The fast-food industry is no exception where restaurants need to be highly flexible and agile in their work. With this, there exists an immense demand for knowledge and insights to help restaurants plan their daily operations and there is a great need for o
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Lindström, Johan. "Predictive maintenance for a wood chipper using supervised machine learning." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149304.

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With a predictive model that can predict failures of a manufacturing machine, many benefits can be obtained. Unnecessary downtime and accidents can be avoided. In this study a wood chipper which has 12 replaceable knives was examined. The specific task was to create a predictive model that can predict if a knife change is needed or not. To create a predictive model, supervised machine learning was used. Decision forest was the algorithm used in this study. Data samples were collected from vibration measurements. Each sample was labeled with help of ocular inspections of the knives. Microsoft A
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Creps, Merl John Jr. "A Supervised Machine Learning Approach Using Object-Oriented Programming Principles." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1524674114609993.

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Campbell, Benjamin W. "Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1558024695617708.

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Han, Kun. "Supervised Speech Separation And Processing." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1407865723.

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Rosell, Mikael. "Semi-Supervised Learning for Object Detection." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-113560.

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Many automotive safety applications in modern cars make use of cameras and object detection to analyze the surrounding environment. Pedestrians, animals and other vehicles can be detected and safety actions can be taken before dangerous situations arise. To detect occurrences of the different objects, these systems are traditionally trained to learn a classification model using a set of images that carry labels corresponding to their content. To obtain high performance with a variety of object appearances, the required amount of data is very large. Acquiring unlabeled images is easy, while the
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Fox-Roberts, Patrick Kirk. "An examination of the causes of bias in semi-supervised learning." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648460.

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Xiang, Gao, and Jiang Nan. "Supervised Methods for Fault Detection in Vehicle." Thesis, Högskolan i Halmstad, Intelligenta system (IS-lab), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14151.

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Uptime and maintenance planning are important issues for vehicle operators (e.g.operators of bus fleets). Unplanned downtime can cause a bus operator to be fined if the vehicle is not on time. Supervised classification methods for detecting faults in vehicles are compared in this thesis. Data has been collected by a vehicle manufacturer including three kinds of faulty states in vehicles (i.e. charge air cooler leakage, radiator and air filter clogging). The problem consists of differentiating between the normal data and the three different categories of faulty data. Evaluated methods include l
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Kégl, Balazs. "Contributions to machine learning: the unsupervised, the supervised, and the Bayesian." Habilitation à diriger des recherches, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00674004.

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Hussein, Abdul Aziz. "Identifying Crime Hotspot: Evaluating the suitability of Supervised and Unsupervised Machine learning." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624914607243042.

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Stakovska, Meri. "Improving search results with machine learning : Classifying multi-source data with supervised machine learning to improve search results." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75598.

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Sony’s Support Application team wanted an experiment to be conducted by which they could determine if it was suitable to use Machine Learning to improve the quantity and quality of search results of the in-application search tool. By improving the quantity and quality of the results the team wanted to improve the customer’s journey. A supervised machine learning model was created to classify articles into four categories; Wi-Fi &amp; Connectivity, Apps &amp; Settings, System &amp; Performance, andBattery Power &amp; Charging. The same model was used to create a service that categorized the sea
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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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Berdin, Davide. "PARLA: mobile application for English pronunciation. A supervised machine learning approach." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303549.

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Learning and improving a second language is fundamental in the globalised world we live in. In particular, English is the common tongue used everyday by billions of people and the necessity of having good pronunciation in order to avoid misunderstanding is higher then ever. Smartphones and other mobile devices have rapidly become an every-day technology with endless potential given the large size of screens as well as the high portability. Old-fashioned language courses are very useful and important, however using the technology for picking up a new language in an automatic way with less time
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Ruiz, Ovejero Adrià. "Weakly-supervised learning for automatic facial behaviour analysis." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/457708.

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In this Thesis we focus on Automatic Facial Behavior Analysis, which attempts to develop autonomous systems able to recognize and understand human facial expressions. Given the amount of information expressed by facial gestures, this type of systems has potential applications in multiple domains such as Human Computer Interaction, Marketing or Healthcare. For this reason, the topic has attracted a lot of attention in Computer Vision and Machine Learning communities during the past two decades. Despite the advances in the field, most of facial expression analysis problems can be considered far
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Amershi, Saleema Amin. "Combining unsupervised and supervised machine learning to build user models for intelligent learning environments." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31622.

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Traditional approaches to developing user models, especially for computer-based learning environments, are notoriously difficult and time-consuming because they rely heavily on expert-elicited knowledge about the target application and domain. Furthermore, because the expert-elicited knowledge used in the user model is application and domain specific, the entire model development process must be repeated for each new application. In this thesis, we outline a data-based user modeling framework that uses both unsupervised and supervised machine learning in order to reduce the development
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Allberg, Petrus. "Applied machine learning in the logistics sector : A comparative analysis of supervised learning algorithms." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16656.

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BackgroundMachine learning is an area that is being explored with great haste these days, which inspired this study to investigate how seven different supervised learning algorithms perform compared to each other. These algorithms were used to perform classification tasks on logistics consignments, the classification is binary and a consignment can either be classified as missed or not. ObjectivesThe goal was to find which of these algorithms perform well when used for this classification task and to see how the results varied with different sized datasets. Importance of the features which wer
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Ringqvist, Sanna. "Classification of terrain using superpixel segmentation and supervised learning." Thesis, Linköpings universitet, Datorseende, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112511.

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The usage of 3D-modeling is expanding rapidly. Modeling from aerial imagery has become very popular due to its increasing number of both civilian and mili- tary applications like urban planning, navigation and target acquisition. This master thesis project was carried out at Vricon Systems at SAAB. The Vricon system produces high resolution geospatial 3D data based on aerial imagery from manned aircrafts, unmanned aerial vehicles (UAV) and satellites. The aim of this work was to investigate to what degree superpixel segmentation and supervised learning can be applied to a terrain classificatio
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Thomforde, Emily Jane. "Semi-supervised lexical acquisition for wide-coverage parsing." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/7741.

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State-of-the-art parsers suffer from incomplete lexicons, as evidenced by the fact that they all contain built-in methods for dealing with out-of-lexicon items at parse time. Since new labelled data is expensive to produce and no amount of it will conquer the long tail, we attempt to address this problem by leveraging the enormous amount of raw text available for free, and expanding the lexicon offline, with a semi-supervised word learner. We accomplish this with a method similar to self-training, where a fully trained parser is used to generate new parses with which the next generation of par
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Durand, Thibaut. "Weakly supervised learning for visual recognition." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066142/document.

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Cette thèse s'intéresse au problème de la classification d'images, où l'objectif est de prédire si une catégorie sémantique est présente dans l'image, à partir de son contenu visuel. Pour analyser des images de scènes complexes, il est important d'apprendre des représentations localisées. Pour limiter le coût d'annotation pendant l'apprentissage, nous nous sommes intéressé aux modèles d'apprentissage faiblement supervisé. Dans cette thèse, nous proposons des modèles qui simultanément classifient et localisent les objets, en utilisant uniquement des labels globaux pendant l'apprentissage. L'app
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46

Korecki, John Nicholas. "Semi-Supervised Self-Learning on Imbalanced Data Sets." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1686.

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Semi-supervised self-learning algorithms have been shown to improve classifier accuracy under a variety of conditions. In this thesis, semi-supervised self-learning using ensembles of random forests and fuzzy c-means clustering similarity was applied to three data sets to show where improvement is possible over random forests alone. Two of the data sets are emulations of large simulations in which the data may be distributed. Additionally, the ratio of majority to minority class examples in the training set was altered to examine the effect of training set bias on performance when applying the
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47

Lavesson, Niklas. "Evaluation and Analysis of Supervised Learning Algorithms and Classifiers." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2006. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/c655a0b1f9f88d16c125714c00355e5d?OpenDocument.

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48

Djuric, Nemanja. "Big Data Algorithms for Visualization and Supervised Learning." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/239445.

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Computer and Information Science<br>Ph.D.<br>Explosive growth in data size, data complexity, and data rates, triggered by emergence of high-throughput technologies such as remote sensing, crowd-sourcing, social networks, or computational advertising, in recent years has led to an increasing availability of data sets of unprecedented scales, with billions of high-dimensional data examples stored on hundreds of terabytes of memory. In order to make use of this large-scale data and extract useful knowledge, researchers in machine learning and data mining communities are faced with numerous challe
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49

Durand, Thibaut. "Weakly supervised learning for visual recognition." Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066142.

Full text
Abstract:
Cette thèse s'intéresse au problème de la classification d'images, où l'objectif est de prédire si une catégorie sémantique est présente dans l'image, à partir de son contenu visuel. Pour analyser des images de scènes complexes, il est important d'apprendre des représentations localisées. Pour limiter le coût d'annotation pendant l'apprentissage, nous nous sommes intéressé aux modèles d'apprentissage faiblement supervisé. Dans cette thèse, nous proposons des modèles qui simultanément classifient et localisent les objets, en utilisant uniquement des labels globaux pendant l'apprentissage. L'app
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50

Karkare, Rasika S. "Machine learning approaches to manufacturing and materials: Applications to semi-supervised, unbalanced and heterogeneous data problems." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1341.

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The objective of this thesis is to use machine learning and deep learning techniques for the quality assurance of metal casting processes. Metal casting can be defined as a process in which liquid metal is poured into a mold of a desired shape and allowed to solidify. The process is completed after ejection of the final solidified component, also known as a casting, out of the mold. There may be undesired irregularities in the metal casting process known as casting defects. Among the defects that are found, porosity is considered to be a major defect, which is difficult to detect, until the en
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