Relevant bibliographies by topics / Systems for Machine Learning / Dissertations / Theses
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Dissertations / Theses on the topic 'Systems for Machine Learning'

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Published: 6 September 2023

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1

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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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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Swere, Erick A. R. "Machine learning in embedded systems." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/4969.

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This thesis describes novel machine learning techniques specifically designed for use in real-time embedded systems. The techniques directly address three major requirements of such learning systems. Firstly, learning must be capable of being achieved incrementally, since many applications do not have a representative training set available at the outset. Secondly, to guarantee real-time performance, the techniques must be able to operate within a deterministic and limited time bound. Thirdly, the memory requirement must be limited and known a priori to ensure the limited memory available to h
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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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Roderus, Jens, Simon Larson, and Eric Pihl. "Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.

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The amount of available data has allowed the field of machine learning to flourish. But with growing data set sizes comes an increase in algorithm execution times. Cluster computing frameworks provide tools for distributing data and processing power on several computer nodes and allows for algorithms to run in feasible time frames when data sets are large. Different cluster computing frameworks come with different trade-offs. In this thesis, the scalability of the execution time of machine learning algorithms running on the Hadoop cluster computing framework is investigated. A recent version o
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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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Schneider, C. "Using unsupervised machine learning for fault identification in virtual machines." Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.

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Self-healing systems promise operating cost reductions in large-scale computing environments through the automated detection of, and recovery from, faults. However, at present there appears to be little known empirical evidence comparing the different approaches, or demonstrations that such implementations reduce costs. This thesis compares previous and current self-healing approaches before demonstrating a new, unsupervised approach that combines artificial neural networks with performance tests to perform fault identification in an automated fashion, i.e. the correct and accurate determinati
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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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Ilyas, Andrew. "On practical robustness of machine learning systems." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122911.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 71-79).<br>We consider the importance of robustness in evaluating machine learning systems, an in particular systems involving deep learning. We consider these systems' vulnerability to adversarial examples--subtle, crafted pe
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ROSA, BRUSIN ANN MARGARETH. "Machine Learning Applications to Optical Communication Systems." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2967019.

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Thomas, Sabin M. (Sabin Mammen). "A system analysis of improvements in machine learning." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/100386.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, February 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 50-51).<br>Machine learning algorithms used for natural language processing (NLP) currently take too long to complete their learning function. This slow learning performance tends to make the model ineffective for an increasing requirement for real time applications such as voice transcription, language translation, text summarization topic extr
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Tynong, Anton. "Machine learning for planning in warehouse management." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178108.

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13

Chi, Chih-Lin Street William N. "Medical decision support systems based on machine learning." Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/283.

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Hsu, David. "Silicon primitives for machine learning /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/6909.

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Chi, Chih-Lin. "Medical decision support systems based on machine learning." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/283.

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This dissertation discusses three problems from different areas of medical research and their machine learning solutions. Each solution is a distinct type of decision support system. They show three common properties: personalized healthcare decision support, reduction of the use of medical resources, and improvement of outcomes. The first decision support system assists individual hospital selection. This system can help a user make the best decision in terms of the combination of mortality, complication, and travel distance. Both machine learning and optimization techniques are utilized in t
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Eagle, Nathan Norfleet. "Machine perception and learning of complex social systems." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32498.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.<br>Includes bibliographical references (p. 125-136).<br>The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, l
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Prueller, Hans. "Distributed online machine learning for mobile care systems." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10875.

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Telecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable wi
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Yang, Yizhan. "Machine Learning Based Beam Tracking in mmWave Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292754.

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The demand for high data rates communication and scarcity of spectrum in existing microwave bands has been the key aspect in 5G. To fulfill these demands, the millimeter wave (mmWave) with large bandwidths has been proposed to enhance the efficiency and the stability of the 5G network. In mmWave communication, the concentration of the transmission signal from the antenna is conducted by beamforming and beam tracking. However, state-of-art methods in beam tracking lead to high resource consumption. To address this problem, we develop 2 machine-learning-based solutions for overhead reduction. In
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Al-Khoury, Fadi. "Safety of Machine Learning Systems in Autonomous Driving." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218020.

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Machine Learning, and in particular Deep Learning, are extremely capable tools for solving problems which are difficult, or intractable to tackle analytically. Application areas include pattern recognition, computer vision, speech and natural language processing. With the automotive industry aiming for increasing amount of automation in driving, the problems to solve become increasingly complex, which appeals to the use of supervised learning methods from Machine Learning and Deep Learning. With this approach, solutions to the problems are learned implicitly from training data, and inspecting
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Salehi, Shahin. "Machine Learning for Contact Mechanics from Surface Topography." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76531.

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Ball, N. R. "Cognitive maps in Learning Classifier Systems." Thesis, University of Reading, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280670.

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Li, Cui. "Image quality assessment using algorithmic and machine learning techniques." Thesis, Available from the University of Aberdeen Library and Historic Collections Digital Resources. Restricted: no access until June 2, 2014, 2009. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?application=DIGITOOL-3&owner=resourcediscovery&custom_att_2=simple_viewer&pid=26521.

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Thesis (Ph.D.)--Aberdeen University, 2009.<br>With: An image quality metric based in corner, edge and symmetry maps / Li Cui, Alastair R. Allen. With: An image quality metric based on a colour appearance model / Li Cui and Alastair R. Allen. ACIVS / J. Blanc-Talon et al. eds. 2008 LNCS 5259, 696-707. Includes bibliographical references.
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Tarberg, Alexander. "Skydd av Kritisk Infrastruktur med Machine Learning." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74514.

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Syftet med detta arbete är att utvärdera om Nash-strategi kan vara ett alternativ till Stackelberg för skydd av tågstationer, med hjälp av Machine Learning och spelteori. Detta arbete beskriver utvecklingen och testning av två algoritmer i en simulerad miljö, schack. Schack fungerade som bra testmiljö, för att få konkreta resultat och poängsättning. Samtidigt fick undertecknad bekräftelse att Nash-strategin fungerade på spel där all information erhålls.      Dessa resultat och den bäst presterande algoritmen användes för att bestämma på vilka stationer man bör placera NBK-sensorer, som skyddar
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Badayos, Noah Garcia. "Machine Learning-Based Parameter Validation." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/47675.

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As power system grids continue to grow in order to support an increasing energy demand, the system's behavior accordingly evolves, continuing to challenge designs for maintaining security. It has become apparent in the past few years that, as much as discovering vulnerabilities in the power network, accurate simulations are very critical. This study explores a classification method for validating simulation models, using disturbance measurements from phasor measurement units (PMU). The technique used employs the Random Forest learning algorithm to find a correlation between specific model para
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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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Arnold, Naomi (Naomi Aiko). "Wafer defect prediction with statistical machine learning." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105633.

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Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. In conjunction with the Leaders for Global Operations Program at MIT.<br>Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 81-83).<br>In the semiconductor industry where the technology continues to grow in complexity while also striving to achieve lower man
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Thomson, John D. "Using machine learning to automate compiler optimisation." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3194.

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Many optimisations in modern compilers have been traditionally based around using analysis to examine certain aspects of the code; the compiler heuristics then make a decision based on this information as to what to optimise, where to optimise and to what extent to optimise. The exact contents of these heuristics have been carefully tuned by experts, using their experience, as well as analytical tools, to produce solid performance. This work proposes an alternative approach – that of using proper statistical analysis to drive these optimisation goals instead of human intuition, through the use
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He, Haibo. "Dynamically Self-reconfigurable Systems for Machine Intelligence." Ohio University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1152717376.

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Niemi, Mikael. "Machine Learning for Rapid Image Classification." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97375.

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In this thesis project techniques for training a rapid image classifier that can recognize an object of a predefined type has been studied. Classifiers have been trained with the AdaBoost algorithm, with and without the use of Viola-Jones cascades. The use of Weight trimming in the classifier training has been evaluated and resulted in a significant speed up of the training, as well as improving the performance of the trained classifier. Different preprocessings of the images have also been tested, but resulted for the most part in worse performance for the classifiers when used individually.
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Bepler, Tristan(Tristan Wendland). "Machine learning for understanding protein sequence and structure." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129888.

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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 183-200).<br>Proteins are the fundamental building blocks of life, carrying out a vast array of functions at the molecular level. Understanding these molecular machines has been a core problem in biology for decades. Recent advances in cryo-electron microscopy (cryoEM) has enabled high resolution experimental measurement of proteins in their native states. However, this technology remain
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Tham, Alan (Alan An Liang). "A guiding framework for applying machine learning in organizations." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107598.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 93-97).<br>Machine Learning (ML) is an emerging business capability that have transformed many organizations by enabling them to learn from past data and helping them predict or make decisions on unknown future events. While ML is no longer the preserve of large IT companies, there are abundant opportunities for mid-sized org
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Sheikholeslami, Sina. "Ablation Programming for Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-258413.

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As machine learning systems are being used in an increasing number of applications from analysis of satellite sensory data and health-care analytics to smart virtual assistants and self-driving cars they are also becoming more and more complex. This means that more time and computing resources are needed in order to train the models and the number of design choices and hyperparameters will increase as well. Due to this complexity, it is usually hard to explain the effect of each design choice or component of the machine learning system on its performance.A simple approach for addressing this p
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Tripathi, Nandita. "Two-level text classification using hybrid machine learning techniques." Thesis, University of Sunderland, 2012. http://sure.sunderland.ac.uk/3305/.

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Nowadays, documents are increasingly being associated with multi-level category hierarchies rather than a flat category scheme. To access these documents in real time, we need fast automatic methods to navigate these hierarchies. Today’s vast data repositories such as the web also contain many broad domains of data which are quite distinct from each other e.g. medicine, education, sports and politics. Each domain constitutes a subspace of the data within which the documents are similar to each other but quite distinct from the documents in another subspace. The data within these domains is fre
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Adjodah, Dhaval D. K. (Adjodlah Dhaval Dhamnidhi Kumar). "Understanding social influence using network analysis and machine learning." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81111.

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Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 61-62).<br>If we are to enact better policy, fight crime and decrease poverty, we will need better computational models of how society works. In order to make computational social science a useful reality, we will need generative models of how social influence sprouts at the interpersonal level and how it leads to emergent social behavior. In this thesis, I take steps at understanding the predictor
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Liu, Han. "Rule based systems for classification in machine learning context." Thesis, University of Portsmouth, 2015. https://researchportal.port.ac.uk/portal/en/theses/rule-based-systems-for-classification-in-machine-learning-context(1790225c-ceb1-48d3-9e05-689edbfa3ef1).html.

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This thesis introduces a unified framework for design of rule based systems for classification tasks, which consists of the operations of rule generation, rule simplification and rule representation. This thesis also stresses the importance of combination of different rule learning algorithms through ensemble learning approaches. For the three operations mentioned above, novel approaches are developed and validated by comparing with existing ones for advancing the performance of using this framework. In particular, for rule generation, Information Entropy Based Rule Generation is developed and
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Testi, Enrico. "Machine Learning for User Traffic Classification in Wireless Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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With the advent of Internet of Things telecommunications will play a crucial role in every day life. The rapidly growing demand for radio services by millions of user all over the world will make the radio spectrum an increasingly valuable resource. The modern standards of communications provide a static utilization of the radio spectrum resources, which results in its under-utilization. Therefore let us imagine a dynamic sharing of the radio resources, where every device can use a portion of such resources if and only if they are not utilized yet. In this regard, the Federal Communicati
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Berral, García Josep Lluís. "Improved self-management of datacenter systems applying machine learning." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/134360.

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Autonomic Computing is a Computer Science and Technologies research area, originated during mid 2000's. It focuses on optimization and improvement of complex distributed computing systems through self-control and self-management. As distributed computing systems grow in complexity, like multi-datacenter systems in cloud computing, the system operators and architects need more help to understand, design and optimize manually these systems, even more when these systems are distributed along the world and belong to different entities and authorities. Self-management lets these distributed computi
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Sonal, Manish. "Machine Learning for PAPR Distortion Reduction in OFDM Systems." Thesis, KTH, Signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-197682.

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The purpose of the project is to investigate the possibility of using modern machine learning to model nonlinear analog devices like the Power Amplifier (PA), and study the feasibility of using such models in wireless systems design. Orthogonal frequency division multiplexing (OFDM) is one of the most prominent modulation technique used in several standards like 802.11a,802.11n, 802.11ac and more. Telecommunication systems like LTE, LTE/Aand WiMAX are also based on OFDM. Nevertheless, OFDM system shows high peak to average power (PAPR) in time domain because it comprises of many subcarriers ad
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Maglaras, Leandros. "Intrusion detection in SCADA systems using machine learning techniques." Thesis, University of Huddersfield, 2018. http://eprints.hud.ac.uk/id/eprint/34578/.

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Modern Supervisory Control and Data Acquisition (SCADA) systems are essential for monitoring and managing electric power generation, transmission and distribution. In the age of the Internet of Things, SCADA has evolved into big, complex and distributed systems that are prone to conventional in addition to new threats. So as to detect intruders in a timely and efficient manner a real time detection mechanism, capable of dealing with a range of forms of attacks is highly salient. Such a mechanism has to be distributed, low cost, precise, reliable and secure, with a low communication overhead, t
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Grzeidak, Emerson. "Identification of nonlinear systems based on extreme learning machine." reponame:Repositório Institucional da UnB, 2016. http://repositorio.unb.br/handle/10482/21603.

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Dissertação (mestrado)—Universidade de Brasília, Programa de Pós-Graduação em Sistemas Mecatrônicos, 2016.<br>Submitted by Camila Duarte (camiladias@bce.unb.br) on 2016-09-14T17:33:55Z No. of bitstreams: 1 2016_EmersonGrzeidak.pdf: 5274560 bytes, checksum: 0f649b217c325601c125fad908bc164f (MD5)<br>Approved for entry into archive by Raquel Viana(raquelviana@bce.unb.br) on 2016-10-21T18:14:35Z (GMT) No. of bitstreams: 1 2016_EmersonGrzeidak.pdf: 5274560 bytes, checksum: 0f649b217c325601c125fad908bc164f (MD5)<br>Made available in DSpace on 2016-10-21T18:14:35Z (GMT). No. of bitstreams: 1 2016_Em
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Barkrot, Felicia, and Mathias Berggren. "Using machine learning for control systems in transforming environments." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166984.

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The development of computational power is constantly on the rise and makes for new possibilities in a lot of areas. Two of the areas that has made great progress thanks to this development are control theory and artificial intelligence. The most eminent area of artificial intelligence is machine learning. The difference between an environment controlled by control theory and an environment controlled by machine learning is that the machine learning model will adapt in order to achieve a goal while the classic model needs preset parameters. This supposedly makes the machine learning model more
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ANSARI, NAZLI. "MACHINE LEARNING METHODS TO IMPROVE NETWORK INTRUSION DETECTION SYSTEMS." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2605.

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Adurti, Devi Abhiseshu, and Mohit Battu. "Optimization of Heterogeneous Parallel Computing Systems using Machine Learning." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21834.

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Background: Heterogeneous parallel computing systems utilize the combination of different resources CPUs and GPUs to achieve high performance and, reduced latency and energy consumption. Programming applications that target various processing units requires employing different tools and programming models/languages. Furthermore, selecting the most optimal implementation, which may either target different processing units (i.e. CPU or GPU) or implement the various algorithms, is not trivial for a given context. In this thesis, we investigate the use of machine learning to address the selection
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Beale, Dan. "Autonomous visual learning for robotic systems." Thesis, University of Bath, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558886.

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This thesis investigates the problem of visual learning using a robotic platform. Given a set of objects the robots task is to autonomously manipulate, observe, and learn. This allows the robot to recognise objects in a novel scene and pose, or separate them into distinct visual categories. The main focus of the work is in autonomously acquiring object models using robotic manipulation. Autonomous learning is important for robotic systems. In the context of vision, it allows a robot to adapt to new and uncertain environments, updating its internal model of the world. It also reduces the amount
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45

Kostiadis, Kostas. "Learning to co-operate in multi-agent systems." Thesis, University of Essex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248696.

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Kanwar, John. "Smart cropping tools with help of machine learning." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74827.

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Machine learning has been around for a long time, the applications range from a big variety of different subjects, everything from self driving cars to data mining. When a person takes a picture with its mobile phone it easily happens that the photo is a little bit crooked. It does also happen that people takes spontaneous photos with help of their phones, which can result in something irrelevant ending up in the corner of the image. This thesis combines machine learning with photo editing tools. It will explore the possibilities how machine learning can be used to automatically crop images in
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Drugowitsch, Jan. "Learning classifier systems from first principles : a probabilistic reformulation of learning classifier systems from the perspective of machine learning." Thesis, University of Bath, 2007. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500684.

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Learning Classifier Systems (LCS) are a family of rule-based machine learning methods. They aim at the autonomous production of potentially human readable results that are the most compact generalised representation whilst also maintaining high predictive accuracy, with a wide range of application areas, such as autonomous robotics, economics, and multi-agent systems. Their design is mainly approached heuristically and, even though their performance is competitive in regression and classification tasks, they do not meet their expected performance in sequential decision tasks despite being init
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Panholzer, Georg. "Identifying Deviating Systems with Unsupervised Learning." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1146.

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<p>We present a technique to identify deviating systems among a group of systems in a</p><p>self-organized way. A compressed representation of each system is used to compute similarity measures, which are combined in an affinity matrix of all systems. Deviation detection and clustering is then used to identify deviating systems based on this affinity matrix.</p><p>The compressed representation is computed with Principal Component Analysis and</p><p>Kernel Principal Component Analysis. The similarity measure between two compressed</p><p>representations is based on the angle between the spaces s
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Hellborg, Per. "Optimering av datamängder med Machine learning : En studie om Machine learning och Internet of Things." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13747.

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This report is about how an Internet of Things (IoT) optimization can be done with Machine learning (ML). The IoT- devices in this report are sensors in containers that read how full the containers are. The report contains a case from Sogeti. Were a client can use this optimization to get better routes for their garbage truck, with this solution the garbage trucks will only go to full containers and be able to skip empty or close to empty containers. This will result in less fuel costs and a better environment. This solution can be used for every industry that needs a route optimization. To do
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Tataru, Augustin. "Metrics for Evaluating Machine Learning Cloud Services." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Datateknik och informatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-37882.

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Machine Learning (ML) is nowadays being offered as a service by several cloud providers. Consumers require metrics to be able to evaluate and compare between multiple ML cloud services. There aren’t many established metrics that can be used specifically for these types of services. In this paper, the Goal-QuestionMetric paradigm is used to define a set of metrics applicable for ML cloud services. The metrics are created based on goals expressed by professionals who use or are interested in using these services. At the end, a questionnaire is used to evaluate the metrics based on two criteria:
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