Relevant bibliographies by topics / Nearest Neighbour / Dissertations / Theses
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Dissertations / Theses on the topic 'Nearest Neighbour'

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Published: 4 June 2021
Last updated: 25 July 2025

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1

Lloyd, Michael. "Nearest neighbour epidemic processes." Thesis, Heriot-Watt University, 1994. http://hdl.handle.net/10399/747.

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2

Kibriya, Ashraf Masood. "Fast Algorithms for Nearest Neighbour Search." The University of Waikato, 2007. http://hdl.handle.net/10289/2463.

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The nearest neighbour problem is of practical significance in a number of fields. Often we are interested in finding an object near to a given query object. The problem is old, and a large number of solutions have been proposed for it in the literature. However, it remains the case that even the most popular of the techniques proposed for its solution have not been compared against each other. Also, many techniques, including the old and popular ones, can be implemented in a number of ways, and often the different implementations of a technique have not been thoroughly compared either. This
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Bermejo, Sánchez Sergio. "Learning with nearest neighbour classifiers." Doctoral thesis, Universitat Politècnica de Catalunya, 2000. http://hdl.handle.net/10803/6323.

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Premi extraordinari ex-aequo en l'àmbit d'Electrònica i Telecomunicacions. Convocatoria 1999 - 2000<br>Nearest Neighbour (NN) classifiers are one of the most celebrated algorithms in machine learning. In recent years, interest in these methods has flourished again in several fields (including statistics, machine learning and pattern recognition) since, in spite of their simplicity, they reveal as powerful non-parametric classification systems in real-world problems. The present work is mainly devoted to the development of new learning algorithms for these classifiers and is focused on the foll
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Asyhari, Agustian Taufiq. "Nearest neighbour decoding for fading channels." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610448.

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Mittinty, Murthy N. "Nearest neighbour imputation and variance estimation methods." Thesis, University of Canterbury. Mathematics and Statistics, 2004. http://hdl.handle.net/10092/5643.

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In large-scale surveys, non-response is a common phenomenon. This non-response can be of two types; unit and item non-response. In this thesis we deal with item non-response as other responses from the survey unit can be used for adjustment. Usually non-response adjustment is carried out in one of three ways; weighting, imputation and no adjustments. Imputation is the most commonly used adjustment method, either as single imputation or multiple imputations. In this thesis we study single imputation, in particular nearest neighbour methods, and we have developed a new method. Our method is base
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6

Shehu, Usman Gulumbe. "Cube technique for Nearest Neighbour(s) search." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248365.

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7

Muja, Marius. "Scalable nearest neighbour methods for high dimensional data." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44402.

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For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbour matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbour matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in thi
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Payne, Terry R. "Dimensionality reduction and representation for nearest neighbour learning." Thesis, University of Aberdeen, 1999. https://eprints.soton.ac.uk/257788/.

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An increasing number of intelligent information agents employ Nearest Neighbour learning algorithms to provide personalised assistance to the user. This assistance may be in the form of recognising or locating documents that the user might find relevant or interesting. To achieve this, documents must be mapped into a representation that can be presented to the learning algorithm. Simple heuristic techniques are generally used to identify relevant terms from the documents. These terms are then used to construct large, sparse training vectors. The work presented here investigates an alternative
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9

Casselryd, Oskar, and Filip Jansson. "Troll detection with sentiment analysis and nearest neighbour search." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209474.

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Internet trolls are gaining more influence in society due to the rapidgrowth of social media. A troll farm is a group of Internet trolls that get paid to spread certain opinions or information online. Identifying a troll farm can be difficult, since the trolls try to stay hidden. This study examines if it is possible to identify troll farms on Twitter by conducting a sentiment analysis on user tweets and modeling it as a nearest neighbor problem. The experiment was done with 4 simulated trolls and 150 normal twitter users. The users were modelled into datapoints based on the sentiment, frequen
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Chandgotia, Nishant. "Markov random fields and measures with nearest neighbour Gibbs potential." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/37000.

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This thesis will discuss the relationship between stationary Markov random fields and probability measures with a nearest neighbour Gibbs potential. While the relationship has been well explored when the measures are fully supported, we shall discuss what happens when we weaken this assumption.
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11

Guo, Gongde. "A study on the nearest neighbour method and its applications." Thesis, University of Ulster, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407756.

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12

Hatko, Stan. "k-Nearest Neighbour Classification of Datasets with a Family of Distances." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33361.

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The k-nearest neighbour (k-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the k-NN classifier. In this thesis we investigate the use of alternative distances for the k-NN classifier. We start by introducing some background notions in statistical machine learning. We define the k-NN classifier and discuss Stone's theorem and the proof that k-NN is universally consistent on the normed space R^d. We then prove that k-NN is universally consistent if we take a sequence of
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13

Lazar, Iustin. "A multi-level nearest-neighbour algorithm for predicting protein secondary structure." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ39987.pdf.

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14

Luk, Andrew. "Some new results in nearest neighbour classification and lung sound analysis." Thesis, University of Glasgow, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280756.

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15

Wasito, Ito. "Least squares algorithms with nearest neighbour techniques for imputing missing data values." Thesis, Birkbeck (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406224.

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16

Berrett, Thomas Benjamin. "Modern k-nearest neighbour methods in entropy estimation, independence testing and classification." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/267832.

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Nearest neighbour methods are a classical approach in nonparametric statistics. The k-nearest neighbour classifier can be traced back to the seminal work of Fix and Hodges (1951) and they also enjoy popularity in many other problems including density estimation and regression. In this thesis we study their use in three different situations, providing new theoretical results on the performance of commonly-used nearest neighbour methods and proposing new procedures that are shown to outperform these existing methods in certain settings. The first problem we discuss is that of entropy estimation.
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Stiff, Philip, and Carl Holmqvist. "Jämförelse av avståndsmått för K-nearest neighbour-klassificering av resmål hos nya användare." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186354.

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Användarbeteende är idag ett område som blir allt viktigare för företag som vill erbjuda tjänster anpassade efter sina kunder. För att kunna konkurrera på marknaden vill företagen kunna föreslå sina kunder en tjänst redan innan kunderna vet om att de behöver den. Det finns ett flertal kända algoritmer för att uppnå detta. I denna studie undersöks K-nearest neighbour-algoritmen och hur den bör anpassas för att mäta likhet mellan instanser av kunder i en databas. För att göra detta jämförs en egenutvecklad metod baserad på instansernas generella förhållanden med några befintliga metoder. Jämföre
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Faustino, Bruno Filipe Fernandes Simões Salgueiro. "Implementation for spatial data of the shared nearest neighbour with metric data structures." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8489.

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Stiernborg, Sebastian, and Sara Ervik. "Evaluation of Machine Learning Classification Methods : Support Vector Machines, Nearest Neighbour and Decision Tree." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209119.

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With more and more data available, the interest and use for machine learning is growing and so does the need for classification. Classifica- tion is an important method within machine learning for data simpli- fication and prediction. This report evaluates three classification methods for supervised learn- ing: Support Vector Machines (SVM) with several kernels, Nearest Neighbor (k-NN) and Decision Tree (DT). The methods were evalu- ated based on the factors accuracy, precision, recall and time. The experiments were conducted on artificial data created to represent a variation of distributions
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Zandi, Zand Sajjad. "FPGA implementation of ROI extraction for visual-IR smart cameras." Thesis, Mittuniversitetet, Avdelningen för elektronikkonstruktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-26076.

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Video surveillance systems have been popular as a security tool for years, and the technological development helps monitoring accident-prone areas with the help of digital image processing.A thermal and a visual camera are being used in the surveillance project. The thermal camera is sensitive to the heat emitted by objects, and it is essential to employ the thermal camera as the visual camera is only useful in the presence of light. These cameras do not provide images of the same resolution. In order to extract the region of interest (ROI) of the visual camera, the images of these cameras nee
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Borén, Mirjam. "Classification of discrete stress levels in users using eye tracker and K- Nearest Neighbour algorithm." Thesis, Umeå universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-176258.

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The advancement of the Head Mounted Display (HMD) used for Virtual Reality (VR) has come a long way and now the option of eye tracking is available in some HMD. The eyes show physiological responses when healthy individuals are stressed, justifying eye tracking as a tool to estimate at minimum, the very presence of stress. Stress can present itself in many shapes and may be caused by different factors such as work, social situations, cognitive load and many others. The stress test Group Stroop Color Word Test (GSCWT) can induce four different levels of stress in users; no stress, low stress, m
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22

O'Hare, Anthony. "The formation of low-temperature superstructures in the two-dimensional Ising model with next-nearest neighbour interactions." Thesis, Loughborough University, 2007. https://dspace.lboro.ac.uk/2134/36011.

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For several decades the formation of different kinds of superstructures in solids has been a topical issue in condensed matter physics. The superstructures (or spatially modulated structures) may be of a different nature: magnetic patterns like spin-density waves, inhomogeneous charge distributions in charge-ordered compounds, dipolar and quadrupolar ordering in ferroelectrics or ferroelastics, regular lattice distortions and related orbital structures, stripe-like arrangements of dopants in alloys, etc. The phase diagrams of such compounds can be rather complicated involving a large number of
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Chielo, Leonard Ikenna. "The use of range, behaviour, nearest neighbour distance and feather condition of commercial free-range laying hens." Thesis, University of Lincoln, 2017. http://eprints.lincoln.ac.uk/28625/.

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This thesis is based on the evidence of the use of range by laying hens from two separate studies that were carried out on commercial flocks. The first study involved a direct observation of 6 flocks of laying hens whereas the second study was carried out on a single flock of laying hens using a still digital camera. Mapping of the outdoor range into three zones, namely; apron (0-10metres), enriched (10-50metres) and range (beyond 50metres), was carried out in both studies determine whether it influenced the distribution, behaviour, nearest neighbour distance (NND) and feather condition of the
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Dalinger, Alexander [Verfasser], Volker [Akademischer Betreuer] Betz, and Frank [Akademischer Betreuer] Aurzada. "On the hydrodynamic behaviour of a particle system with nearest neighbour interactions / Alexander Dalinger ; Volker Betz, Frank Aurzada." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2019. http://d-nb.info/1198403438/34.

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25

Ferm, Martin. "Prediktering av skogliga variabler med data från flygburen laser : En jämförelse mellan multipla regressionsmodeller och k nearest neighbour-modeller." Thesis, Umeå University, Department of Statistics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-34833.

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26

Liang, Wen. "Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery." Click here to access this resource online, 2009. http://hdl.handle.net/10292/749.

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“Machine learning is the process of discovering and interpreting meaningful information, such as new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Larose, 2005). From my understanding, machine learning is a process of using different analysis techniques to observe previously unknown, potentially meaningful information, and discover strong patterns and relationships from a large dataset. Professor Kasabov (2007b) classified computational models into
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Mason, Ben Ross. "A comparison of approaches for implementation of k-nearest neighbour imputation for missing items in cross-national, time series data sets of economic indicators." Thesis, University of Southampton, 2018. https://eprints.soton.ac.uk/422171/.

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The need for predictive accuracy in the imputation for missing data in cross‐national, time series data is discussed and the possibility of requiring unconventional approaches to imputation, namely approaches which are tailored to the specific context and applied to individual instances of missing items is also discussed. Potential barriers to moving toward such an approach are mentioned and in particular, the demands on resources implied by that. A taxonomy of available observations is established with the aim of being able to use it to quickly and efficiently identify potential solutions for
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Bertilsson, Tobias, and Romario Johansson. "Undersökning om hjulmotorströmmar kan användas som alternativ metod för kollisiondetektering i autonoma gräsklippare. : Klassificering av hjulmotorströmmar med KNN och MLP." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Datateknik och informatik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-43555.

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Purpose – The purpose of the study is to expand the knowledge of how wheel motor currents can be combined with machine learning to be used in a collision detection system for autonomous robots, in order to decrease the number of external sensors and open new design opportunities and lowering production costs. Method – The study is conducted with design science research where two artefacts are developed in a cooperation with Globe Tools Group. The artefacts are evaluated in how they categorize data given by an autonomous robot in the two categories collision and non-collision. The artefacts are
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Holub, Anna, and Jie Liu. "Recognizing Combustion Variability for Control of Gasoline Engine Exhaust Gas Recirculation using Information from the Ion Current." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-235.

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<p>The ion current measured from the spark plug in a spark ignited combustion engine is used </p><p>as basis for analysis and control of the combustion variability caused by exhaust gas </p><p>recirculation. Methods for extraction of in-cylinder pressure information from the ion </p><p>current are analyzed in terms of reliability and processing efficiency. A model for the </p><p>recognition of combustion variability using this information is selected and tested on both </p><p>simulated and car data.</p>
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Glawing, Henrik. "Measurement data selection and association in a collision mitigation system." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1233.

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<p>Today many car manufactures are developing systems that help the driver to avoid collisions. Examples of this kind of systems are: adaptive cruise control, collision warning and collision mitigation / avoidance. </p><p>All these systems need to track and predict future positions of surrounding objects (vehicles ahead of the system host vehicle), to calculate the risk of a future collision. To validate that a prediction is correct the predictions must be correlated to observations. This is called the data association problem. If a prediction can be correlated to an observation, this observat
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Vršecká, Renáta. "Optimalizace rozvozu piva společnosti Heineken." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-76523.

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This thesis deals with real logistic problem of the Heineken CZ Company. The company sets down an itinerary for each vehicle to distribute its goods to particular customers on daily basis. These itineraries are created manually, only with the skill of experienced driver. The goal of this thesis is to find a solution with an algorithm, which will be able to set optimal itineraries of all vehicles, so the total distance and therefore operating costs are minimized, with only the knowledge of distances between each two nodes.
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Cirincione, Antonio. "Algoritmi di Machine Learning per la Classificazione di Dati Inerziali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Questo studio tratta l'implementazione di due algoritmi di Machine Learning per il riconoscimento di tre attività motorie: camminata, corsa e andare in bicicletta. Mediante l'applicazione per smartphone Matlab Mobile sono state acquisii profili di accelerazione da tre utenti, due usati in fase di Test ed uno usato in fase di Training degli algoritmi. Si sono classificate tali attività estraendo delle opportune feature di interesse alla classificazione, in particolare la deviazione standard del''accelerazione che si è rivelata una buona scelta per discriminare le attività motorie. Gli algorit
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Puplinskaitė, Donata. "Autoregresinių procesų ir atsitiktinių laukų su baigtine arba begaline dispersija agregavimas." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2013~D_20131029_102452-85316.

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Agreguoti duomenys naudojami daugelyje mokslo sričių tokių kaip ekonomika, sociologija, geografija ir kt. Tai motyvuoja tirti (de)agregavimo uždavinį. Viena iš pagrindinių priežasčių kodėl vienalaikis agregavimas tapo tyrimų objektu yra galimybė gauti ilgos atminties procesus. Agregavimas paaiškina ilgos atminties atsiradima procesuose ir yra vienas iš būdų tokius procesus generuoti. Agreguodami trumpos atminties neergodiškus atsitiktinius procesus, galime gauti ilgos atminties ergodišką procesą, kuris gali būti naudojamas mikro ir makro kintamųjų prognozavimui. Disertacijoje nagrinėjama AR(1)
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Gustavsson, Oscar, and Adam Miksits. "Inomhuspositionering med bredbandig radio." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254253.

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In this report it is evaluated whether a higher dimensional fingerprint vector increases accuracy of an algorithm for indoor localisation. Many solutions use a Received Signal Strength Indicator (RSSI) to estimate a position. It was studied if the use of the Channel State Information (CSI), i.e. the channel’s frequency response, is beneficial for the accuracy.The localisation algorithm estimates the position of a new measurement by comparing it to previous measurements using k-Nearest Neighbour (k-NN) regression. The mean power was used as RSSI and 100 samples of the frequency response as CSI.
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Bernardi, Elena. "Tecniche di Machine Learning per la classificazione di accelerazioni indotte da mezzi di trasporto." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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In questo elaborato si è raggiunto l'obiettivo di realizzare un algoritmo di Machine Learning per il riconoscimento di accelerazioni di mezzi di trasporto. Le accelerazioni sono state acquisite tramite un sensore accelerometrico situato all'interno degli smartphone, grazie all'applicazione Matlab Mobile. I dati accelerometrici sono stati successivamente caricati sul pc per essere studiati ed elaborati attraverso Matlab. Sono state individuate le differenze tra dati di accelerazioni di mezzi di trasporto e accelerazioni di attività motorie. Poi si è potuto procedere con l'identificazione
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Puplinskaitė, Donata. "Aggregation of autoregressive processes and random fields with finite or infinite variance." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2013~D_20131029_102339-22917.

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Aggregated data appears in many areas such as econimics, sociology, geography, etc. This motivates an importance of studying the (dis)aggregation problem. One of the most important reasons why the contemporaneous aggregation become an object of research is the possibility of obtaining the long memory phenomena in processes. The aggregation provides an explanation of the long-memory effect in time series and a simulation method of such series as well. Accumulation of short-memory non-ergodic random processes can lead to the long memory ergodic process, that can be used for the forecasts of the
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Solnická, Veronika. "Návrh a aplikace heuristických metod při rozvozu objednávek zákazníkům společnosti NIKOL NÁPOJE a. s." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-72124.

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This thesis deals with the optimization of distribution of products to consumers based on a real case study of a particular company from Opava. For this purpose, a mathematical optimization model is used to illustrate the vehicle routing problem. The study will also offer an explanation on the relevancy of heuristic methods, mainly with respect to their application in solving real life situations analogous to the one surveyed. On the basis of chosen heuristic methods (i.e. the nearest neighbour algorithm and the savings algorithm) and having taken into account the restricting conditions of the
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Liu, Julia, and Linnéa Lindahl. "Prediktion av efterfrågan i filmbranschen baserat på maskininlärning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235719.

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Machine learning is a central technology in data-driven decision making. In this study, machine learning in the context of demand forecasting in the motion picture industry from film exhibitors’ perspective is investigated. More specifically, it is investigated to what extent the technology can assist estimation of public interest in terms of revenue levels of unreleased movies. Three machine learning models are implemented with the aim to forecast cumulative revenue levels during the opening weekend of various movies which were released in 2010-2017 in Sweden. The forecast is based on ten att
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Truong, Thanh. "Indexing Nearest Neighbor Queries." Thesis, Uppsala University, Department of Information Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-129464.

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<p>In database technology, one very well known problem is K nearest neighbor (KNN). However, the cost of finding a solution of the KNN problem may be expensive with the increase of database size. In order to achieve efficient data mining of large amounts of data, it is important to index high dimensional data to support KNN search.</p><p>Xtree, an index structure for high dimensional data, was investigated and then integrated into Amos II, an extensible functional Database Management System (DBMS). The result of the integration is AmosXtree, which has showed that the query time for KNN search
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Iofcea, Markus. "Föderierte Nearest-Neighbor-Queries." [S.l. : s.n.], 2003. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11126555.

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Shen, Qiong Mao. "Group nearest neighbor queries /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20SHEN.

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Hui, Michael Chun Kit. "Aggregate nearest neighbor queries /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20HUI.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 91-95). Also available in electronic version. Access restricted to campus users.
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Gebauerová, Monika. "Optimalizace rozvozu pekárenských výrobků." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-124644.

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This thesis deals with the optimization of distribution of bakery products. Firstly there are the fundamental types of vehicle routing problems and their optimization models introduced. Next part is dedicated to heuristic algorithms. The heuristic methods are introduced in general, then there are the chosen methods described. Later there are two chosen algorithms formulated. First one based on the nearest neighbour method and another one based on the savings algorithm. Both of algorithms were programmed in the Visual Basic of Applications MS Excel 2010. These algorithms were applied for the so
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Åkerblom, Thea, and Tobias Thor. "Fraud or Not?" Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388695.

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This paper uses statistical learning to examine and compare three different statistical methods with the aim to predict credit card fraud. The methods compared are Logistic Regression, K-Nearest Neighbour and Random Forest. They are applied and estimated on a data set consisting of nearly 300,000 credit card transactions to determine their performance using classification of fraud as the outcome variable. The three models all have different properties and advantages. The K-NN model preformed the best in this paper but has some disadvantages, since it does not explain the data but rather predic
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Kravciv, Zbyněk. "Optimalizace rozvozu a svozu infuzních roztoků." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-17384.

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There are many distribution tasks, that vary in a number of vehicles, time windows, dividend or undivided delivery or if it is static problems or dynamic problems. In this essay I focus just on few of them. At first I put my mind to simple statistic distribution task with one vehicle. Later I extend it with time windows, when the point can be served by one car and by many cars. In the essay it will be solved the real task of distribution and delivery transportation of infusion in the hospitals. Because of the difficulty of solution I have to use the heuristic methods - Method of nearest neighb
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Landmér, Pedersen Jesper. "Weighing Machine Learning Algorithms for Accounting RWISs Characteristics in METRo : A comparison of Random Forest, Deep Learning & kNN." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-85586.

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The numerical model to forecast road conditions, Model of the Environment and Temperature of Roads (METRo), laid the foundation of solving the energy balance and calculating the temperature evolution of roads. METRo does this by providing a numerical modelling system making use of Road Weather Information Stations (RWIS) and meteorological projections. While METRo accommodates tools for correcting errors at each station, such as regional differences or microclimates, this thesis proposes machine learning as a supplement to the METRo prognostications for accounting station characteristics. Cont
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Luong, Khanh Thi Ngoc. "Clustering methods for multi-aspect data." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/131620/1/Thi%20Ngoc%20Khanh%20Luong%20Thesis.pdf.

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With the rapid growth of computational technology, multi-aspect data has become ubiquitous and popular. Multi-aspect data is able to represent the information with multiple perspectives or multiple types of features. This thesis explores this hot topic of machine learning and presents several methods of clustering by exploiting the multi-aspect data properties under the Non-negative Matrix Factorization framework with manifold learning. The proposed methods have shown superiority to identify subgroups for the high-dimensional, sparse and complex data. The proposed methods have applicability to
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Chappell, Timothy A. "Scalable document hashing and retrieval." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/90044/1/Timothy_Chappell_Thesis.pdf.

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This thesis studies document signatures, which are small representations of documents and other objects that can be stored compactly and compared for similarity. This research finds that document signatures can be effectively and efficiently used to both search and understand relationships between documents in large collections, scalable enough to search a billion documents in a fraction of a second. Deliverables arising from the research include an investigation of the representational capacity of document signatures, the publication of an open-source signature search platform and an approach
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Karo, Ciril. "Two new nearest neighbor classification rules." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA354997.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, September 1998.<br>"September 1998." Thesis advisor(s): Samuel E. Buttrey. Includes bibliographical references (p. 69-71). Also available online.
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KUMAR, SUSMIT. "NEAREST NEIGHBOR SEARCH IN DISTRIBUTED DATABASES." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1022879916.

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