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Dissertations / Theses on the topic 'Regression based machine learning'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Thorén, Daniel. "Radar based tank level measurement using machine learning : Agricultural machines." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176259.

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Agriculture is becoming more dependent on computerized solutions to make thefarmer’s job easier. The big step that many companies are working towards is fullyautonomous vehicles that work the fields. To that end, the equipment fitted to saidvehicles must also adapt and become autonomous. Making this equipment autonomoustakes many incremental steps, one of which is developing an accurate and reliable tanklevel measurement system. In this thesis, a system for tank level measurement in a seedplanting machine is evaluated. Traditional systems use load cells to measure the weightof the tank however

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2

Bagheri, Rajeoni Alireza. "ANALOG CIRCUIT SIZING USING MACHINE LEARNING BASED TRANSISTORCIRCUIT MODEL." University of Akron / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=akron1609428170125214.

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3

Qader, Aso, and William Shiver. "Developing an Advanced Internal Ratings-Based Model by Applying Machine Learning." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273418.

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Since the regulatory framework Basel II was implemented in 2007, banks have been allowed to develop internal risk models for quantifying the capital requirement. By using data on retail non-performing loans from Hoist Finance, the thesis assesses the Advanced Internal Ratings-Based approach. In particular, it focuses on how banks active in the non-performing loan industry, can risk-classify their loans despite limited data availability of the debtors. Moreover, the thesis analyses the effect of the maximum-recovery period on the capital requirement. In short, a comparison of five different mat

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4

Ekman, Björn. "Machine Learning for Beam Based Mobility Optimization in NR." Thesis, Linköpings universitet, Kommunikationssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136489.

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One option for enabling mobility between 5G nodes is to use a set of area-fixed reference beams in the downlink direction from each node. To save power these reference beams should be turned on only on demand, i.e. only if a mobile needs it. An User Equipment (UE) moving out of a beam's coverage will require a switch from one beam to another, preferably without having to turn on all possible beams to find out which one is the best. This thesis investigates how to transform the beam selection problem into a format suitable for machine learning and how good such solutions are compared to baselin

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Börthas, Lovisa, and Sjölander Jessica Krange. "Machine Learning Based Prediction and Classification for Uplift Modeling." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266379.

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The desire to model the true gain from targeting an individual in marketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investiga

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Blomkvist, Oscar. "Machine Learning Based Sentiment Classification of Text, with Application to Equity Research Reports." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257506.

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In this thesis, we analyse the sentiment in equity research reports written by analysts at Skandinaviska Enskilda Banken (SEB). We provide a description of established statistical and machine learning methods for classifying the sentiment in text documents as positive or negative. Specifically, a form of recurrent neural network known as long short-term memory (LSTM) is of interest. We investigate two different labelling regimes for generating training data from the reports. Benchmark classification accuracies are obtained using logistic regression models. Finally, two different word embedding

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Faraj, Dina. "Using Machine Learning for Predictive Maintenance in Modern Ground-Based Radar Systems." Thesis, KTH, Matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299634.

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Military systems are often part of critical operations where unplanned downtime should be avoided at all costs. Using modern machine learning algorithms it could be possible to predict when, where, and at what time a fault is likely to occur which enables time for ordering replacement parts and scheduling maintenance. This thesis is a proof of concept study for anomaly detection in monitoring data, i.e., sensor data from a ground based radar system as an initial experiment to showcase predictive maintenance. The data in this thesis was generated by a Giraffe 4A during normal operation, i.e., n

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Li, Xinfeng. "Image based human body rendering via regression & MRF energy minimization." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5188.

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A machine learning method for synthesising human images is explored to create new images without relying on 3D modelling. Machine learning allows the creation of new images through prediction from existing data based on the use of training images. In the present study, image synthesis is performed at two levels: contour and pixel. A class of learning-based methods is formulated to create object contours from the training image for the synthetic image that allow pixel synthesis within the contours in the second level. The methods rely on applying robust object descriptions, dynamic learning mod

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9

Zambonin, Giuliano. "Development of Machine Learning-based technologies for major appliances: soft sensing for drying technology applications." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3425771.

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In this thesis, Machine Learning techniques for the improvements in the performance of household major appliances are described. In particular, the focus is on drying technologies and domestic dryers are the machines of interest selected as case studies. Statistical models called Soft Sensors have been developed to provide estimates of quantities that are costly/time-consuming to measure in our applications using data that were available for other purposes. The work has been developed as industrially driven research activity in collaborations with Electrolux Italia S.p.a. R&D department locat

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Kornfeld, Sarah. "Predicting Default Probability in Credit Risk using Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275656.

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This thesis has explored the field of internally developed models for measuring the probability of default (PD) in credit risk. As regulators put restrictions on modelling practices and inhibit the advance of risk measurement, the fields of data science and machine learning are advancing. The tradeoff between stricter regulation on internally developed models and the advancement of data analytics was investigated by comparing model performance of the benchmark method Logistic Regression for estimating PD with the machine learning methods Decision Trees, Random Forest, Gradient Boosting and Art

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11

Maragno, Donato. "Optimization with machine learning-based modeling: an application to humanitarian food aid." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21621/.

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In this thesis, we propose a machine learning-based optimization methodology to build (part of) optimization models with a data-driven approach. This approach is useful whenever we have to model one or more relations between the decisions and their impact on the system. This kind of relationship can be challenging to model manually, and so machine learning is used to learn it through the use of data. We demonstrate the potential of this method through a case study in which a predictive model is used to approximate the palatability scoring function in a typical diet problem formulation. Firs

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Blank, Clas, and Tomas Hermansson. "A Machine Learning approach to churn prediction in a subscription-based service." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240397.

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Prenumerationstjänster blir alltmer populära i dagens samhälle. En av nycklarna för att lyckas med en prenumerationsbaserad affärsmodell är att minimera kundbortfall (eng. churn), dvs. kunder som avslutar sin prenumeration inom en viss tidsperiod. I och med den ökande digitaliseringen, är det nu enklare att samla in data än någonsin tidigare. Samtidigt växer maskininlärning snabbt och blir alltmer lättillgängligt, vilket möjliggör nya infallsvinklar på problemlösning. Denna rapport kommer testa och utvärdera ett försök att förutsäga kundbortfall med hjälp av maskininlärning, baserat på kunddat

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O'Leary, Brian. "A Vertex-Based Approach to the Statistical and Machine Learning Analyses of Brain Structure." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1576254162111087.

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Tout, Hicham Refaat. "Measuring the Impact of email Headers on the Predictive Accuracy of Machine Learning Techniques." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/325.

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The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning algorithms to detect phishing attack did increase the predictive accuracy of these learning algorithms. The same research also recommended further investigation of the impact of including an expanded set of email headers on the predictive accuracy of machine

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Yang, Ruochen. "Diagnosis of Evaporative Emissions Control System Using Physics-based and Machine Learning Methods." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587651390226087.

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Dissanayake, Lekamlage Dilukshi Charitha Subashini Dissanayake, and Fabia Afzal. "AI-based Age Estimation from Mammograms." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20108.

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Background: Age estimation has attracted attention because of its various clinical and medical applications. There are many studies on human age estimation from biomedical images such as X-ray images, MRI, facial images, dental images etc. However, there is no research done on mammograms for age estimation. Therefore, in our research, we focus on age estimation from mammogram images. Objectives: The purpose of this study is to make an AI-based model for estimating age from mammogram images based on the pectoral muscle segment and check its accuracy. At first, we segment the pectoral muscle fro

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Kortesalmi, Linus. "Gaussian Process Regression-based GPS Variance Estimation and Trajectory Forecasting." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153126.

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Spatio-temporal data is a commonly used source of information. Using machine learning to analyse this kind of data can lead to many interesting and useful insights. In this thesis project, a novel public transportation spatio-temporal dataset is explored and analysed. The dataset contains 282 GB of positional events, spanning two weeks of time, from all public transportation vehicles in Östergötland county, Sweden. From the data exploration, three high-level problems are formulated: bus stop detection, GPS variance estimation, and arrival time prediction, also called trajectory forecasting. T

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Semprevivo, Riccardo. "Realization and Performance Characterization of a Myoelectric Control System for Robotic Hands Based on Kernel Ridge Regression." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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In the field of human-robot interaction, the research is still far to find a solution to a stable control for hand prosthesis. In particular, one of the most promising methodology is represented by the use of electromyographic signals(EMG) of the muscles as a interface between the human and the artificial limb. The EMG is already used to control robotic systems that present a little number of degrees of freedom (d.o.f.), but for more complex controls able to regulate 6 or more hand's degrees of freedom several problems persist. The nonstationarity of the EMG and the nonlinear relation related

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Salzillo, Vincenzo. "“Myocontrol of Prosthetic Hands: Enforcing Active Learning by a Machine Learning based Fault Detector”." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Improvements in the control of prosthetic hands can provide a future solution for amputated people, improving their motor skills and in general their quality of life. The prosthesis controlled by electromyography (EMG) are studied for their greater potential, but the technology is still unprepared for mass usage. The control is too unstable due to various phenomena related to the inconstancy of bioelectric signals and changes in the human body posture during the execution of tasks. This thesis is focused on the creation of a fault detector able to detect these instabilities and correct the pr

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Kwame, Osei Eric. "Machine Learning-based Quality Prediction in the Froth Flotation Process of Mining : Master’s Degree Thesis in Microdata Analysis." Thesis, Högskolan Dalarna, Mikrodataanalys, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:du-31643.

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In the iron ore mining fraternity, in order to achieve the desired quality in the froth flotation processing plant, stakeholders rely on conventional laboratory test technique which usually takes more than two hours to ascertain the two variables of interest. Such a substantial dead time makes it difficult to put the inherent stochastic nature of the plant system in steady-state. Thus, the present study aims to evaluate the feasibility of using machine learning algorithms to predict the percentage of silica concentrate (SiO2) in the froth flotation processing plant in real-time. The predictive

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21

Tang, Songyin Verfasser], Ralf [Akademischer Betreuer] [Korn, and Ralf [Akademischer Betreuer] Werner. "American-style Option Pricing and Improvement of Regression-based Monte Carlo Methods by Machine Learning Techniques / Songyin Tang. Betreuer: Ralf Korn ; Ralf Werner." Kaiserslautern : Technische Universität Kaiserslautern, 2015. http://d-nb.info/1076503055/34.

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Tang, Songyin [Verfasser], Ralf [Akademischer Betreuer] Korn, and Ralf [Akademischer Betreuer] Werner. "American-style Option Pricing and Improvement of Regression-based Monte Carlo Methods by Machine Learning Techniques / Songyin Tang. Betreuer: Ralf Korn ; Ralf Werner." Kaiserslautern : Technische Universität Kaiserslautern, 2015. http://d-nb.info/1076503055/34.

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23

Johansson, Åke, and Joel Wikner. "Learning-Based Motion Planning and Control of a UGV With Unknown and Changing Dynamics." Thesis, Linköpings universitet, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176923.

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Research about unmanned ground vehicles (UGVs) has received an increased amount of attention in recent years, partly due to the many applications of UGVs in areas where it is inconvenient or impossible to have human operators, such as in mines or urban search and rescue. Two closely linked problems that arise when developing such vehicles are motion planning and control of the UGV. This thesis explores these subjects for a UGV with an unknown, and possibly time-variant, dynamical model. A framework is developed that includes three components: a machine learning algorithm to estimate the unknow

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Buttar, Sarpreet Singh. "Applying Machine Learning to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-77201.

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Self-adaptive systems are capable of autonomously adjusting their behavior at runtime to accomplish particular adaptation goals. The most common way to realize self-adaption is using a feedback loop(s) which contains four actions: collect runtime data from the system and its environment, analyze the collected data, decide if an adaptation plan is required, and act according to the adaptation plan for achieving the adaptation goals. Existing approaches achieve the adaptation goals by using formal methods, and exhaustively verify all the available adaptation options, i.e., adaptation space. Howe

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Brusamento, Donato. "Improving pattern recognition based myocontrol of prosthetic hands via user-in-the-loop." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Il controllo di mani protesiche basato su elettromiografia (EMG) ha le potenzialità di ristabilire funzioni motorie ai pazienti che hanno subito un’amputazione, migliorando sensibilimente la qualità della vita. Tuttavia rimangono problemi aperti nell’ottenere un controllo ricco di movimenti e stabile, fra cui la presenza del limb position effect. La tesi si concentra nel cercare di ridurre questa causa di instabilità, proponendo una versione modificata dell’algoritmo Ridge Regression with Random Fourier Features, reso incrementale e arricchito di feedback all’utente. Questo approccio viene po

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Kim, Byung-Jun. "Semiparametric and Nonparametric Methods for Complex Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99155.

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A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect

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Schmidt, Eric. "Atomistic modelling of precipitation in Ni-base superalloys." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/275131.

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The presence of the ordered $\gamma^{\prime}$ phase ($\text{Ni}_{3}\text{Al}$) in Ni-base superalloys is fundamental to the performance of engineering components such as turbine disks and blades which operate at high temperatures and loads. Hence for these alloys it is important to optimize their microstructure and phase composition. This is typically done by varying their chemistry and heat treatment to achieve an appropriate balance between $\gamma^{\prime}$ content and other constituents such as carbides, borides, oxides and topologically close packed phases. In this work we have set out to

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Lood, Olof. "Prediktering av grundvattennivåi område utan grundvattenrör : Modellering i ArcGIS Pro och undersökningav olika miljövariablers betydelse." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448020.

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Myndigheten Sveriges Geologiska Undersökning (SGU) har ett nationellt ansvar för att övervaka Sveriges grundvattennivåer. Eftersom det inte är möjligt att få ett heltäckande mätstationssystem måste grundvattennivån beräknas på vissa platser. Därför är det intressant att undersöka sambandet mellan grundvattennivån och utvald geografisk information, så kallade miljövariabler. På sikt kan maskininlärning komma att användas inom SGU för att beräkna grundvattennivån och då kan en förstudie vara till stor hjälp. Examensarbetets syfte är att genomföra en sådan förstudie genom att undersöka vilka milj

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Nguyen, Mai. "Machine Learning Algorithmsfor Regression Modeling in Private Insurance." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234857.

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This thesis is focused on the Occupational Pension, an important part of the retiree’s total pension. It is paid by private insurance companies and determined by an annuity divisor. Regression modeling of the annuity divisor is done by using the monthly paid pension as a response and a set of 24 explanatory variables e.g. the expected remaining lifetime and advance interest rate. Two machine learning algorithms, artificial neural networks (ANN) and support vector machines for regression (SVR) are considered in detail. Specifically, different transfer functions for ANN are studied as well as th

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Ekström, Lukas. "Estimating fuel consumption using regression and machine learning." Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235819.

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This thesis focuses on investigating the usage of statistical models for estimating fuel consumption of heavy duty vehicles. Several statistical models are assessed, along with machine learning using artiﬁcial neural networks. Data recorded by sensors on board trucks in the EU describe the operational usage of the vehicle. The usage of this data for estimating the fuel consumption is assessed, and several variables originating from the operational data is modelled and tested as possible input parameters. The estimation model for real world fuel consumption uses 8 parameters describing the oper

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Davies, Alexander James. "Effective implementation of Gaussian process regression for machine learning." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708909.

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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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Andblom, Mikael. "Improved Differential Diagnostics Using Methods in Machine Learning and Regression." Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240083.

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There is a desire both from the patient and the society to have eﬃcient tools for diﬀerential diagnostics. Mathematical relationships between diseases and observable consequences are deﬁned in the thesis. Speciﬁcally artiﬁcial neural networks are considered in the modeling of the doctor’s methodology. To suggest further lab tests or symptoms to look for the network is inverted by looking at a minimization problem where the objective function gradient can be analytically calculated. Due to diﬃculties in obtaining real life medical data a program was constructed to generate artiﬁcial patient dat

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Liu, Xiaoyang. "Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93737.

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Machine learning methods are now extensively applied in various scientific research areas to make models. Unlike regular models, machine learning based models use a data-driven approach. Machine learning algorithms can learn knowledge that are hard to be recognized, from available data. The data-driven approaches enhance the role of algorithms and computers and then accelerate the computation using alternative views. In this thesis, we explore the possibility of applying machine learning models in the prediction of chromatographic retention behaviors. Chromatographic separation is a key techni

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GALLI, FABIAN. "Predicting PV self-consumption in villas with machine learning." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300433.

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In Sweden, there is a strong and growing interest in solar power. In recent years, photovoltaic (PV) system installations have increased dramatically and a large part are distributed grid connected PV systems i.e. rooftop installations. Currently the electricity export rate is significantly lower than the import rate which has made the amount of self-consumed PV electricity a critical factor when assessing the system profitability. Self-consumption (SC) is calculated using hourly or sub-hourly timesteps and is highly dependent on the solar patterns of the location of interest, the PV system co

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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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Pan, Zhiwei. "Statistical learning algorithms : multi-class classification and regression with non-i.i.d. sampling /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ma-b30082316f.pdf.

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Thesis (Ph.D.)--City University of Hong Kong, 2009.<br>"Submitted to Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves [65]-75)

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Torgo, Luís Fernando Raínho Alves. "Inductive learning of tree-based regression models." Doctoral thesis, Universidade do Porto. Reitoria, 1999. http://hdl.handle.net/10216/10018.

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Dissertação de Doutoramento em Ciência de Computadores apresentada à Faculdade de Ciências da Universidade do Porto<br>Esta tese explora diferentes aspectos da metodologia de indução de árvores de regressão a partir de amostras de dados. O objectivo principal deste estudo é o de melhorar a capacidade predictiva das árvores de regressão tentando manter, tanto quanto possível, a sua compreensibilidade e eficiência computacional. O nosso estudo sobre este tipo de modelos de regressão é dividido em três partes principais.Na primeira parte do estudo são descritas em detalhe duas metodologias para c

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Torgo, Luís Fernando Raínho Alves. "Inductive learning of tree-based regression models." Tese, Universidade do Porto. Reitoria, 1999. http://hdl.handle.net/10216/10018.

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Dissertação de Doutoramento em Ciência de Computadores apresentada à Faculdade de Ciências da Universidade do Porto<br>Esta tese explora diferentes aspectos da metodologia de indução de árvores de regressão a partir de amostras de dados. O objectivo principal deste estudo é o de melhorar a capacidade predictiva das árvores de regressão tentando manter, tanto quanto possível, a sua compreensibilidade e eficiência computacional. O nosso estudo sobre este tipo de modelos de regressão é dividido em três partes principais.Na primeira parte do estudo são descritas em detalhe duas metodologias para c

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Flammarion, Nicolas. "Stochastic approximation and least-squares regression, with applications to machine learning." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE056/document.

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De multiples problèmes en apprentissage automatique consistent à minimiser une fonction lisse sur un espace euclidien. Pour l’apprentissage supervisé, cela inclut les régressions par moindres carrés et logistique. Si les problèmes de petite taille sont résolus efficacement avec de nombreux algorithmes d’optimisation, les problèmes de grande échelle nécessitent en revanche des méthodes du premier ordre issues de la descente de gradient. Dans ce manuscrit, nous considérons le cas particulier de la perte quadratique. Dans une première partie, nous nous proposons de la minimiser grâce à un oracle

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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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Conti, Matteo. "Machine Learning Based Programming Language Identification." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20875/.

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L'avvento dell'era digitale ha contribuito allo sviluppo di nuovi settori tecnologici, i quali, per diretta conseguenza, hanno portato alla richiesta di nuove figure professionali capaci di assumere un ruolo chiave nel processo d'innovazione tecnologica. L'aumento di questa richiesta ha interessato particolarmente il settore dello sviluppo del software, a seguito della nascita di nuovi linguaggi di programmazione e nuovi campi a cui applicarli. La componente principale di cui è composto un software, infatti, è il codice sorgente, il quale può essere rappresentato come un archivio di uno o più

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Jan, Steve T. K. "Robustifying Machine Learning based Security Applications." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99862.

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In recent years, machine learning (ML) has been explored and employed in many fields. However, there are growing concerns about the robustness of machine learning models. These concerns are further amplified in security-critical applications — attackers can manipulate the inputs (i.e., adversarial examples) to cause machine learning models to make a mistake, and it's very challenging to obtain a large amount of attackers' data. These make applying machine learning in security-critical applications difficult. In this dissertation, we present several approaches to robustifying three machine lea

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Anne, Aditya. "A WEB BASED MACHINE LEARNING UTILITY." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1196644291.

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Sobehy, Abdallah. "Machine learning based localization in 5G." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS012.

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La localisation est le processus d'estimation de la position d'une entité dans un système de coordonnées local ou global. Les applications de localisation sont largement réparties dans des contextes différents. Dans les événements, le suivi des participants peut sauver des vies pendant des crises. Dans le domaine de la santé, les personnes âgées peuvent être suivies pour répondre à leurs besoins dans des situations critiques comme les chutes. Dans les entrepôts, les robots transférant des produits d'un endroit à un autre nécessitent une connaissance précise de ses positions, la position des pr

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Lutz, Quentin. "Graph-based contributions to machine-learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT010.

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Un graphe est un objet mathématique permettant de représenter des relations entre des entités (appelées nœuds) sous forme d’arêtes. Les graphes sont depuis longtemps un objet d’étude pour différents problèmes allant d’Euler au PageRank en passant par les problèmes de plus courts chemins. Les graphes ont plus récemment trouvé des usages pour l’apprentissage automatique.Avec l’avènement des réseaux sociaux et du web, de plus en plus de données sont représentées sous forme de graphes. Ces graphes sont toujours plus gros, pouvant contenir des milliards de nœuds et arêtes. La conception d’algorithm

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Goutierre, Emmanuel. "Machine learning-based particle accelerator modeling." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG106.

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Les accélérateurs de particules reposent sur des simulations de haute précision pour optimiser la dynamique du faisceau. Ces simulations sont coûteuses en ressources de calcul, rendant leur analyse en temps réel difficilement réalisable. Cette thèse propose de surmonter cette limitation en explorant le potentiel de l'apprentissage automatique pour développer des modèles de substitution des simulations d'accélérateurs de particules. Ce travail se concentre sur ThomX, une source Compton compacte, et introduit deux modèles de substitution : LinacNet et Implicit Neural ODE (INODE). Ces modèles son

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Laczik, Tamás. "Encoding Temporal Healthcare Data for Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299433.

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This thesis contains a review of previous work in the fields of encoding sequential healthcare data and predicting graft- versus- host disease, a medical condition, based on patient history using machine learning. A new encoding of such data is proposed for machine learning purposes. The proposed encoding, called bag of binned weighted events, is a combination of two strategies proposed in previous work, called bag of binned events and bag of weighted events. An empirical experiment is designed to evaluate the predictive performance of the proposed encoding over various binning windows to that

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Falgén, Enqvist Olle. "Cardinality estimation with a machine learning approach." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288909.

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This thesis investigates how three different machine learning models perform on cardinalty estimation for sql queries. All three models were evaluated on three different data sets. The models were tested on both estimating cardinalities when the query just takes information from one table and also a two way join case. Postgresql's own cardinality estimator was used as a baseline. The evaluated models were: Artificial neural networks, random forests and extreme gradient boosted trees. What was found is that the model that performs best is the extreme gradient boosted tree with a tweedie regress

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Lind, Nilsson Rasmus. "Machine learning in logistics : Increasing the performance of machine learning algorithms on two specific logistic problems." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64761.

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Data Ductus, a multination IT-consulting company, wants to develop an AI that monitors a logistic system and looks for errors. Once trained enough, this AI will suggest a correction and automatically right issues if they arise. This project presents how one works with machine learning problems and provides a deeper insight into how cross-validation and regularisation, among other techniques, are used to improve the performance of machine learning algorithms on the defined problem. Three techniques are tested and evaluated in our logistic system on three different machine learning algorithms, n

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