Thematische Bibliographien / Machine Learning in Telecommunications / Dissertationen
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Dissertationen zum Thema „Machine Learning in Telecommunications“

Autor: Grafiati
Veröffentlicht am 7. Juni 2025
Zuletzt aktualisiert am 2. August 2025

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1

Janagam, Anirudh, and Saddam Hossen. "Analysis of Network Intrusion Detection System with Machine Learning Algorithms (Deep Reinforcement Learning Algorithm)." Thesis, Blekinge Tekniska Högskola, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17126.

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Larsson, Fredrik, and Albert Karlsson. "Evaluation of Machine Learning Algorithms to Reduce Paging Signalling in a Telecom Network." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143714.

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In a telecommunications network locating user equipment (paging) is a common procedure. Proposed functionality for 4G and 5G allows for eNB initiated paging via X2 interfaces. In this thesis machine learning algorithms were evaluated in order to reduce page signalling. Additionally, two paging schemes based on machine learning were proposed and compared to a common method of paging through cost models. The results show that signalling cost can be reduced by up to 80%.
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CAZZATO, STEFANIA. "Machine Learning Techniques Applied to Telecommunication Data." Doctoral thesis, Università degli studi di Genova, 2019. http://hdl.handle.net/11567/942536.

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Chapala, Usha Kiran, and Sridhar Peteti. "Continuous Video Quality of Experience Modelling using Machine Learning Model Trees." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17814.

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Adaptive video streaming is perpetually influenced by unpredictable network conditions, whichcauses playback interruptions like stalling, rebuffering and video bit rate fluctuations. Thisleads to potential degradation of end-user Quality of Experience (QoE) and may make userchurn from the service. Video QoE modelling that precisely predicts the end users QoE underthese unstable conditions is taken into consideration quickly. The root cause analysis for thesedegradations is required for the service provider. These sudden changes in trend are not visiblefrom monitoring the data from the underlyi
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Bernabè, Matteo. "Machine learning based traffic analysis and forecast for 5G Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Mobile traffic forecasting is a relatively new research area, which is becoming of fundamental importance for next-generation networks. Proactively knowing the user demand allows the system to allocate resources and apply energy-saving decisions properly. Classical models are limited by the stationary assumption of time sequences and fail to take correlations into account. This work presents results on cellular network traffic analysis and prediction, providing a novel, robust, and precise machine learning model to efficiently and dynamically manage network resources in 5G systems.
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Palapelas, Kantola Philip. "Extreme Quantile Estimation of Downlink Radio Channel Quality." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177657.

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The application area of Fifth Generation New Radio (5G-NR) called Ultra-Reliable and Low-Latency Communication (URLLC) requires a reliability, the probability of receiving and decoding a data packet correctly, of 1 - 10^5. For this requirement to be fulfilled in a resource-efficient manner, it is necessary to have a good estimation of extremely low quan- tiles of the channel quality distribution, so that appropriate resources can be distributed to users of the network system.  This study proposes and evaluates two methods for estimating extreme quantiles of the downlink channel quality distrib
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Landecker, Will. "Interpretable Machine Learning and Sparse Coding for Computer Vision." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/1937.

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Machine learning offers many powerful tools for prediction. One of these tools, the binary classifier, is often considered a black box. Although its predictions may be accurate, we might never know why the classifier made a particular prediction. In the first half of this dissertation, I review the state of the art of interpretable methods (methods for explaining why); after noting where the existing methods fall short, I propose a new method for a particular type of black box called additive networks. I offer a proof of trustworthiness for this new method (meaning a proof that my method does
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Nguyen, Thi Thu Thuy. "A novel approach for practical real-time, machine learning based ip traffic classification." Swinburne Research Bank, 2009. http://hdl.handle.net/1959.3/61268.

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Thesis (PhD) - Swinburne University of Technology, Faculty of Engineering and Industrial Sciences, Centre for Advanced Internet Architectures, 2009.<br>A thesis submitted for the degree of Doctor of Philosophy, Centre for Advanced Internet Architectures, Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, 2009. Typescript. Bibliography: p. 218-240.
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Adapa, Supriya. "TensorFlow Federated Learning: Application to Decentralized Data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Machine learning is a complex discipline. But implementing machine learning models is far less daunting and difficult than it used to be, thanks to machine learning frameworks such as Google’s TensorFlow Federated that ease the process of acquiring data, training models, serving predictions, and refining future results. There are an estimated 3 billion smartphones in the world and 7 billion connected devices. These phones and devices are constantly generating new data. Traditional analytics and machine learning need that data to be centrally collected before it is processed to yield insights,
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Jeon, Sung-eok. "Near-Optimality of Distributed Network Management with a Machine Learning Approach." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16136.

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An analytical framework is developed for distributed management of large networks where each node makes locally its decisions. Two issues remain open. One is whether a distributed algorithm would result in a near-optimal management. The other is the complexity, i.e., whether a distributed algorithm would scale gracefully with a network size. We study these issues through modeling, approximation, and randomized distributed algorithms. For near-optimality issue, we first derive a global probabilistic model of network management variables which characterizes the complex spatial dependence of the
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Sonnert, Adrian. "Predicting inter-frequency measurements in an LTE network using supervised machine learning : a comparative study of learning algorithms and data processing techniques." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148553.

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With increasing demands on network reliability and speed, network suppliers need to effectivize their communications algorithms. Frequency measurements are a core part of mobile network communications, increasing their effectiveness would increase the effectiveness of many network processes such as handovers, load balancing, and carrier aggregation. This study examines the possibility of using supervised learning to predict the signal of inter-frequency measurements by investigating various learning algorithms and pre-processing techniques. We found that random forests have the highest predict
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Ciavarella, Michele. "Volumetric soil moisture evaluation via radio waves propagation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Nowadays technology plays an important role in different sectors of farm management, in particular technologies based on soil moisture assessment have proven to be efficient in helping farmers who need timely techniques to determine crop water requirements. The main weakness of subsurface moisture sensing techniques is usually related both to the lack of cost-effectiveness of measurements and to unsuitable support scales with respect to the extension of the surface to be investigated. In this regard, Wireless Underground Sensor Network are increasingly used non-destructive tool specifically s
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Svensson, Frida. "Scalable Distributed Reinforcement Learning for Radio Resource Management." Thesis, Linköpings universitet, Tillämpad matematik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177822.

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There is a large potential for automation and optimization in radio access networks (RANs) using a data-driven approach to efficiently handle the increase in complexity due to the steep growth in traffic and new technologies introduced with the development of 5G. Reinforcement learning (RL) has natural applications in RAN control loops such as link adaptation, interference management and power control at different timescales commonly occurring in the RAN context. Elevating the status of data-driven solutions in RAN and building a new, scalable, distributed and data-friendly RAN architecture wi
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Holm, Rasmus. "Prediction of Inter-Frequency Measurements in a LTE Network with Deep Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151879.

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The telecommunications industry faces difficult challenges as more and more devices communicate over the internet. A telecommunications network is a complex system with many parts and some are candidates for further automation. We have focused on interfrequency measurements that are used during inter-frequency handovers, among other procedures. A handover is the procedure when for instance a phone changes the base station it communicates with and the inter-frequency measurements are rather expensive to perform. More specifically, we have investigated the possibility of using deep learning—an e
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Reinholdsen, Fredrik. "A Blind Constellation Agnostic VAE Channel Equalizer and Non Data-Assisted Synchronization." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-86062.

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High performance and high bandwidth wireless digital communication underlies much of modern society. Due to its high value to society, new and improved digital communication technologies, allowing even higher speeds, better coverage, and lower latency are constantly being developed. The field of Machine Learning has exploded in recent years, showing incredible promise and performance at many tasks in a wide variety of fields. Channel Equalization and synchronization are critical parts of any wireless communication system, to ensure coherence between the transmitter and receiver, and to compens
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Mejdi, Sami. "Encoder-Decoder Networks for Cloud Resource Consumption Forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291546.

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Excessive resource allocation in telecommunications networks can be prevented by forecasting the resource demand when dimensioning the networks and the allocation the necessary resources accordingly, which is an ongoing effort to achieve a more sustainable development. In this work, traffic data from cloud environments that host deployed virtualized network functions (VNFs) of an IP Multimedia Subsystem (IMS) has been collected along with the computational resource consumption of the VNFs. A supervised learning approach was adopted to address the forecasting problem by considering encoder-deco
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Mejdi, Sami. "Encoder-Decoder Networks for Cloud Resource Consumption Forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294066.

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Excessive resource allocation in telecommunications networks can be prevented by forecasting the resource demand when dimensioning the networks and then allocating the necessary resources accordingly, which is an ongoing effort to achieve a more sustainable development. In this work, traffic data from cloud environments that host deployed virtualized network functions (VNFs) of an IP Multimedia Subsystem (IMS) has been collected along with the computational resource consumption of the VNFs. A supervised learning approach was adopted to address the forecasting problem by considering encoder-dec
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Olsson, Jakob. "Measuring the Technical and Process Benefits of Test Automation based on Machine Learning in an Embedded Device." Thesis, KTH, Programvaruteknik och datorsystem, SCS, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231785.

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Learning-based testing is a testing paradigm that combines model-based testing with machine learning algorithms to automate the modeling of the SUT, test case generation, test case execution and verdict construction. A tool that implements LBT been developed at the CSC school at KTH called LBTest. LBTest utilizes machine learning algorithms with off-the-shelf equivalence- and model-checkers, and the modeling of user requirements by propositional linear temporal logic. In this study, it is be investigated whether LBT may be suitable for testing a micro bus architecture within an embedded teleco
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Magnusson, Jonathan. "Social Network Analysis Utilizing Big Data Technology." Thesis, Uppsala universitet, Avdelningen för datalogi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170926.

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As of late there has been an immense increase of data within modern society. This is evident within the field of telecommunications. The amount of mobile data is growing fast. For a telecommunication operator, this provides means of getting more information of specific subscribers. The applications of this are many, such as segmentation for marketing purposes or detection of churners, people about to switching operator. Thus the analysis and information extraction is of great value. An approach of this analysis is that of social network analysis. Utilizing such methods yields ways of finding t
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Lieti, Valerio. "Development of an Industrial IoT End-to-End Use Case." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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This project is born merging my personal interest in IoT and AI fields and the collaboration with WiLab, Bi-rex and EBWorld. By joining different skills, the final result is the realization of an end-to-end Industrial IoT application. This IIoT use-case focuses on monitoring accelerations that characterize a turntable, on which mechanical parts are processed. The purpose of the monitoring is to implement predictive maintenance to report potential malfunctions. The turntable in question belongs to DMG Mori, a five-axis turning and milling machine for subtractive manufacturing, machine located a
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Narasimha, Rajesh. "Application of Information Theory and Learning to Network and Biological Tomography." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19889.

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Studying the internal characteristics of a network using measurements obtained from endhosts is known as network tomography. The foremost challenge in measurement-based approaches is the large size of a network, where only a subset of measurements can be obtained because of the inaccessibility of the entire network. As the network becomes larger, a question arises as to how rapidly the monitoring resources (number of measurements or number of samples) must grow to obtain a desired monitoring accuracy. Our work studies the scalability of the measurements with respect to the size of the network.
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Andersson, Viktor. "Machine Learning in Logistics: Machine Learning Algorithms : Data Preprocessing and Machine Learning Algorithms." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64721.

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Data Ductus is a Swedish IT-consultant company, their customer base ranging from small startups to large scale cooperations. The company has steadily grown since the 80s and has established offices in both Sweden and the US. With the help of machine learning, this project will present a possible solution to the errors caused by the human factor in the logistic business.A way of preprocessing data before applying it to a machine learning algorithm, as well as a couple of algorithms to use will be presented.<br>Data Ductus är ett svenskt IT-konsultbolag, deras kundbas sträcker sig från små start
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Collazo, Santiago Bryan Omar. "Machine learning blocks." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100301.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<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.<br>This work presents MLBlocks, a machine learning system that lets data scientists explore the space of modeling techniques in a very easy and efficient manner. We show how the system is very general in the sense that virtually a
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Shukla, Ritesh. "Machine learning ecosystem : implications for business strategy centered on machine learning." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/107342.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, System Design and Management Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 48-50).<br>As interest for adopting machine learning as a core component of a business strategy increases, business owners face the challenge of integrating an uncertain and rapidly evolving technology into their organization, and depending on this for the success of their strategy. The field of Machine learning has a r
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Huembeli, Patrick. "Machine learning for quantum physics and quantum physics for machine learning." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672085.

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Research at the intersection of machine learning (ML) and quantum physics is a recent growing field due to the enormous expectations and the success of both fields. ML is arguably one of the most promising technologies that has and will continue to disrupt many aspects of our lives. The way we do research is almost certainly no exception and ML, with its unprecedented ability to find hidden patterns in data, will be assisting future scientific discoveries. Quantum physics on the other side, even though it is sometimes not entirely intuitive, is one of the most successful physical theories and
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Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Nella presente tesi di laurea viene preso in considerazione l’algoritmo di classificazione Support Vector Machine. Piu` in particolare si considera la sua formulazione come problema di ottimizazione Mixed Integer Program per la classificazione binaria super- visionata di un set di dati.
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Menke, Joshua E. "Improving machine learning through oracle learning /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1726.pdf.

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Menke, Joshua Ephraim. "Improving Machine Learning Through Oracle Learning." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/843.

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The following dissertation presents a new paradigm for improving the training of machine learning algorithms, oracle learning. The main idea in oracle learning is that instead of training directly on a set of data, a learning model is trained to approximate a given oracle's behavior on a set of data. This can be beneficial in situations where it is easier to obtain an oracle than it is to use it at application time. It is shown that oracle learning can be applied to more effectively reduce the size of artificial neural networks, to more efficiently take advantage of domain experts by approxima
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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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Pace, Aaron J. "Guided Interactive Machine Learning." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1355.pdf.

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Montanez, George D. "Why Machine Learning Works." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1114.

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To better understand why machine learning works, we cast learning problems as searches and characterize what makes searches successful. We prove that any search algorithm can only perform well on a narrow subset of problems, and show the effects of dependence on raising the probability of success for searches. We examine two popular ways of understanding what makes machine learning work, empirical risk minimization and compression, and show how they fit within our search frame-work. Leveraging the “dependence-first” view of learning, we apply this knowledge to areas of unsupervised time-series
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Thomaz, Andrea Lockerd. "Socially guided machine learning." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36160.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.<br>Includes bibliographical references (p. 139-146).<br>Social interaction will be key to enabling robots and machines in general to learn new tasks from ordinary people (not experts in robotics or machine learning). Everyday people who need to teach their machines new things will find it natural for to rely on their interpersonal interaction skills. This thesis provides several contributions towards the understanding of this Socially Guided Machine Learning sc
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Leather, Hugh. "Machine learning in compilers." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/9810.

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Tuning a compiler so that it produces optimised code is a difficult task because modern processors are complicated; they have a large number of components operating in parallel and each is sensitive to the behaviour of the others. Building analytical models on which optimisation heuristics can be based has become harder as processor complexity increased and this trend is bound to continue as the world moves towards further heterogeneous parallelism. Compiler writers need to spend months to get a heuristic right for any particular architecture and these days compilers often support a wide range
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Armani, Luca. "Machine Learning: Customer Segmentation." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24925/.

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Con lo scopo di risparmiare capitale e incrementare i profitti tramite attività di marketing sempre più mirate, conoscere le preferenze di un cliente e supportarlo nell’acquisto, sta passando dall’essere una scelta all’essere una necessità. A tal proposito, le aziende si stanno muovendo verso un approccio sempre più automatizzato per riuscire a classificare la clientela, cos`ı da ottimizzare sempre più l’esperienza d’acquisto. Tramite il Machine Learning è possibile effettuare svariati tipi di analisi che consentano di raggiungere questo scopo. L’obiettivo di questo progetto è, in prima fase
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Du, Buisson Lise. "Machine learning in astronomy." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15502.

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The search to find answers to the deepest questions we have about the Universe has fueled the collection of data for ever larger volumes of our cosmos. The field of supernova cosmology, for example, is seeing continuous development with upcoming surveys set to produce a vast amount of data that will require new statistical inference and machine learning techniques for processing and analysis. Distinguishing between real objects and artefacts is one of the first steps in any transient science pipeline and, currently, is still carried out by humans - often leading to hand scanners having to sort
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Punugu, Venkatapavani Pallavi. "Machine Learning in Neuroimaging." Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10284048.

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<p> The application of machine learning algorithms to analyze and determine disease related patterns in neuroimaging has emerged to be of extreme interest in Computer-Aided Diagnosis (CAD). This study is a small step towards categorizing Alzheimer's disease, Neurode-generative diseases, Psychiatric diseases and Cerebrovascular Small Vessel diseases using CAD. In this study, the SPECT neuroimages are pre-processed using powerful data reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA) and Automated Anatomical Labeling (AAL). Each of the pre-proc
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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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Johansson, Richard. "Machine learning på tidsseriedataset : En utvärdering av modeller i Azure Machine Learning Studio." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71223.

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In line with technology advancements in processing power and storing capabilities through cloud services, higher demands are set on companies’ data sets. Business executives are now expecting analyses of real time data or massive data sets, where traditional Business Intelligence struggle to deliver. The interest of using machine learning to predict trends and patterns which the human eye can’t see is thus higher than ever. Time series data sets are data sets characterised by a time stamp and a value; for example, a sensor data set. The company with which I’ve been in touch collects data from
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Wu, Anjian M. B. A. Sloan School of Management. "Performance modeling of human-machine interfaces using machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122599.

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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 Electrical Engineering and Computer Science, 2019, In conjunction with the Leaders for Global Operations Program at MIT<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 70-71).<br>As the popularity of online retail expands, world-class electronic commerce (e-commerce) businesses are increasingly adopting collaborative robotics and I
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Ohlsson, Caroline. "Exploring the potential of machine learning : How machine learning can support financial risk management." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324684.

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For decades, there have been developments of computer software to support human decision making. Along with the increased complexity of business environments, smart technologies are becoming popular and useful for decision support based on huge amount of information and advanced analysis. The aim of this study was to explore the potential of using machine learning for financial risk management in debt collection, with a purpose of providing a clear description of what possibilities and difficulties there are. The exploration was done from a business perspective in order to complement previous
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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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Grangier, David. "Machine learning for information retrieval." Lausanne : École polytechnique fédérale de Lausanne, 2008. http://aleph.unisg.ch/volltext/464553_Grangier_Machine_learning_for_information_retrieval.pdf.

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43

Baglioni, Cecilia. "Processi Gaussiani e Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20704/.

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In questa tesi si approfondisce la teoria dei Processi Gaussiani utilizzando i modelli di regressione per arrivare alla distribuzione predittiva a posteriori e studiandone le proprietà di differenziabilità per le Greche: in questo senso si introduce il Machine Learning tramite la tecnica del Supervised Learning. Nel primo capitolo si elencano le ipotesi alla base del modello di Black e Scholes e il procedimento per ottenere la formula. Si arriva, così, alla definizione delle Greche utili a valutare la sensibilità del portafoglio rispetto alla variazione dei fattori da cui la formula di Black e
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Liao, Yihua. "Machine learning in intrusion detection /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2005. http://uclibs.org/PID/11984.

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Strobl, Carolin. "Statistical Issues in Machine Learning." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-89043.

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Stendahl, Jonas, and Johan Arnör. "Gesture Keyboard USING MACHINE LEARNING." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157141.

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The market for mobile devices is expanding rapidly. Input of text is a large part of using a mobile device and an input method that is convenient and fast is therefore very interesting. Gesture keyboards allow the user to input text by dragging a finger over the letters in the desired word. This study investigates if enhancements of gesture keyboards can be accomplished using machine learning. A gesture keyboard was developed based on an algorithm which used a Multilayer Perceptron with backpropagation and evaluated. The results indicate that the evaluated implementation is not an optimal solu
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Bergkvist, Markus, and Tobias Olandersson. "Machine learning in simulated RoboCup." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3827.

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An implementation of the Electric Field Approach applied to the simulated RoboCup is presented, together with a demonstration of a learning system. Results are presented from the optimization of the Electric Field parameters in a limited situation, using the learning system. Learning techniques used in contemporary RoboCup research are also described including a brief presentation of their results.
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Stefan, Vasic, and Lindgren Nicklas. "Product categorisation using machine learning." Thesis, KTH, Data- och elektroteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209031.

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Machine learning is a method in data science for analysing large data sets and extracting hidden patterns and common characteristics in the data. Corporations often have access to databases containing great amounts of data that could contain valuable information. Navetti AB wants to investigate the possibility to automate their product categorisation by evaluating different types of machine learning algorithms. This could increase both time- and cost efficiency. This work resulted in three different prototypes, each using different machine learning algorithms with the ability to categorise pro
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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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Parson, Rupert. "Machine learning of changing concepts." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393757.

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