Дисертації з теми "Large sets"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 дисертацій для дослідження на тему "Large sets".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте дисертації для різних дисциплін та оформлюйте правильно вашу бібліографію.
Ziegler, Albert. "Large sets in constructive set theory." Thesis, University of Leeds, 2014. http://etheses.whiterose.ac.uk/8370/.
Повний текст джерелаKleinberg, Robert David. "Online decision problems with large strategy sets." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33092.
Повний текст джерелаIncludes bibliographical references (p. 165-171).
In an online decision problem, an algorithm performs a sequence of trials, each of which involves selecting one element from a fixed set of alternatives (the "strategy set") whose costs vary over time. After T trials, the combined cost of the algorithm's choices is compared with that of the single strategy whose combined cost is minimum. Their difference is called regret, and one seeks algorithms which are efficient in that their regret is sublinear in T and polynomial in the problem size. We study an important class of online decision problems called generalized multi- armed bandit problems. In the past such problems have found applications in areas as diverse as statistics, computer science, economic theory, and medical decision-making. Most existing algorithms were efficient only in the case of a small (i.e. polynomial- sized) strategy set. We extend the theory by supplying non-trivial algorithms and lower bounds for cases in which the strategy set is much larger (exponential or infinite) and the cost function class is structured, e.g. by constraining the cost functions to be linear or convex. As applications, we consider adaptive routing in networks, adaptive pricing in electronic markets, and collaborative decision-making by untrusting peers in a dynamic environment.
by Robert David Kleinberg.
Ph.D.
Villalba, Michael Joseph. "Fast visual recognition of large object sets." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/42211.
Повний текст джерелаArvidsson, Johan. "Finding delta difference in large data sets." Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74943.
Повний текст джерелаBate, Steven Mark. "Generalized linear models for large dependent data sets." Thesis, University College London (University of London), 2004. http://discovery.ucl.ac.uk/1446542/.
Повний текст джерелаCordeiro, Robson Leonardo Ferreira. "Data mining in large sets of complex data." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-22112011-083653/.
Повний текст джерелаO crescimento em quantidade e complexidade dos dados armazenados nas organizações torna a extração de conhecimento utilizando técnicas de mineração uma tarefa ao mesmo tempo fundamental para aproveitar bem esses dados na tomada de decisões estratégicas e de alto custo computacional. O custo vem da necessidade de se explorar uma grande quantidade de casos de estudo, em diferentes combinações, para se obter o conhecimento desejado. Tradicionalmente, os dados a explorar são representados como atributos numéricos ou categóricos em uma tabela, que descreve em cada tupla um caso de teste do conjunto sob análise. Embora as mesmas tarefas desenvolvidas para dados tradicionais sejam também necessárias para dados mais complexos, como imagens, grafos, áudio e textos longos, a complexidade das análises e o custo computacional envolvidos aumentam significativamente, inviabilizando a maioria das técnicas de análise atuais quando aplicadas a grandes quantidades desses dados complexos. Assim, técnicas de mineração especiais devem ser desenvolvidas. Este Trabalho de Doutorado visa a criação de novas técnicas de mineração para grandes bases de dados complexos. Especificamente, foram desenvolvidas duas novas técnicas de agrupamento e uma nova técnica de rotulação e sumarização que são rápidas, escaláveis e bem adequadas à análise de grandes bases de dados complexos. As técnicas propostas foram avaliadas para a análise de bases de dados reais, em escala de Terabytes de dados, contendo até bilhões de objetos complexos, e elas sempre apresentaram resultados de alta qualidade, sendo em quase todos os casos pelo menos uma ordem de magnitude mais rápidas do que os trabalhos relacionados mais eficientes. Os dados reais utilizados vêm das seguintes aplicações: diagnóstico automático de câncer de mama, análise de imagens de satélites, e mineração de grafos aplicada a um grande grafo da web coletado pelo Yahoo! e também a um grafo com todos os usuários da rede social Twitter e suas conexões. Tais resultados indicam que nossos algoritmos permitem a criação de aplicações em tempo real que, potencialmente, não poderiam ser desenvolvidas sem a existência deste Trabalho de Doutorado, como por exemplo, um sistema em escala global para o auxílio ao diagnóstico médico em tempo real, ou um sistema para a busca por áreas de desmatamento na Floresta Amazônica em tempo real
Chaudhary, Amitabh. "Applied spatial data structures for large data sets." Available to US Hopkins community, 2002. http://wwwlib.umi.com/dissertations/dlnow/3068131.
Повний текст джерелаHennessey, Anthony. "Statistical shape analysis of large molecular data sets." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52088/.
Повний текст джерелаHuet, Benoit. "Object recognition from large libraries of line patterns." Thesis, University of York, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298533.
Повний текст джерелаThorarinsson, Johann Sigurdur. "ruleViz : visualization of large rule sets and composite events." Thesis, University of Skövde, School of Humanities and Informatics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-2286.
Повний текст джерелаEvent Condition Action rule engines have been developed for some time now. Theycan respond automatically to events coming from different sources. Combination ofdifferent event types may be different from time to time and there for it is hard todetermine how the rule engine executes its rules. Especially when the engine is givena large rule set to work with. To determine the behavior is to run tests on the ruleengine and see the final results, but if the results are wrong it can be hard to see whatwent wrong. ruleViz is a program that can look at the execution and visually animatethe rule engine behavior by showing connections between rules and composite events,making it easier for the operator to see what causes the fault. ruleViz is designed toembrace Human Computer Interaction (HCI) methods, making its interfaceunderstandable and easy to operate.
Dementiev, Roman. "Algorithm engineering for large data sets hardware, software, algorithms." Saarbrücken VDM, Müller, 2006. http://d-nb.info/986494429/04.
Повний текст джерелаDementiev, Roman. "Algorithm engineering for large data sets : hardware, software, algorithms /." Saarbrücken : VDM-Verl. Dr. Müller, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=3029033&prov=M&dok_var=1&dok_ext=htm.
Повний текст джерелаNair, Sumitra Sarada. "Function estimation using kernel methods for large data sets." Thesis, University of Sheffield, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444581.
Повний текст джерелаRauschenberg, David Edward. "Computer-graphical exploration of large data sets from teletraffic." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186645.
Повний текст джерелаFarran, Bassam. "One-pass algorithms for large and shifting data sets." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/159173/.
Повний текст джерелаMangalvedkar, Pallavi Ramachandra. "GPU-ASSISTED RENDERING OF LARGE TREE-SHAPED DATA SETS." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1195491112.
Повний текст джерелаYeh, Jieh-Shan George. "Large sets of disjoint t-(v,k,[lambda]) designs /." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193272069635.
Повний текст джерелаAnderson, James D. "Interactive Visualization of Search Results of Large Document Sets." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1547048073451373.
Повний текст джерелаToulis, Panagiotis. "Implicit methods for iterative estimation with large data sets." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493434.
Повний текст джерелаStatistics
Posid, Tasha Irene. "The small-large divide: The development of infant abilities to discriminate small from large sets." Thesis, Boston College, 2015. http://hdl.handle.net/2345/bc-ir:104371.
Повний текст джерелаThesis advisor: Ellen Winner
Evidence suggests that humans and non-human animals have access to two distinct numerical representation systems: a precise "object-file" system used to visually track small quantities (<4) and an approximate, ratio-dependent analog magnitude system used to represent all natural numbers. Although many studies to date indicate that infants can discriminate exclusively small sets (e.g., 1 vs. 2, 2 vs. 3) or exclusively large sets (4 vs. 8, 8 vs. 16), a robust phenomenon exists whereby they fail to compare sets crossing this small-large boundary (2 vs. 4, 3 vs. 6) despite a seemingly favorable ratio of difference between the two set sizes. Despite these robust failures in infancy (up to 14 months), studies suggest that 3-year old children no longer encounter difficulties comparing small from large sets, yet little work has explored the development of this phenomenon between 14 months and 3 years of age. The present study investigates (1) when in development infants naturally overcome this inability to compare small vs. large sets, as well as (2) what factors may facilitate this ability: namely, perceptual variability and/or numerical language. Results from three cross-sectional studies indicate that infants begin to discriminate between small and large sets as early as 17 months of age. Furthermore, infants seemed to benefit from perceptual variability of the items in the set when making these discriminations. Moreover, although preliminary evidence suggests that a child's ability to verbally count may correlate with success on these discriminations, simply exposure to numerical language (in the form of adult modeling of labeling the cardinality and counting the set) does not affect performance
Thesis (PhD) — Boston College, 2015
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Psychology
Ljung, Patric. "Efficient Methods for Direct Volume Rendering of Large Data Sets." Doctoral thesis, Norrköping : Department of Science and Technology, Linköping University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7232.
Повний текст джерелаLam, Heidi Lap Mun. "Visual exploratory analysis of large data sets : evaluation and application." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/839.
Повний текст джерелаTricker, Edward A. "Detecting anomalous aggregations of data points in large data sets." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.512050.
Повний текст джерелаUminsky, David. "Generalized Spectral Analysis for Large Sets of Approval Voting Data." Scholarship @ Claremont, 2003. https://scholarship.claremont.edu/hmc_theses/157.
Повний текст джерелаReuter, Patrick. "Reconstruction and Rendering of Implicit Surfaces from Large Unorganized Point Sets." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2003. http://tel.archives-ouvertes.fr/tel-00576950.
Повний текст джерелаLundell, Fredrik. "Out-of-Core Multi-Resolution Volume Rendering of Large Data Sets." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70162.
Повний текст джерелаMånsson, Per. "Database analysis and managing large data sets in a trading environment." Thesis, Linköpings universitet, Databas och informationsteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104193.
Повний текст джерелаJungic, Veselin. "Elementary, topological, and experimental approaches to the family of large sets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0027/NQ51878.pdf.
Повний текст джерелаCarter, Caleb. "High Resolution Visualization of Large Scientific Data Sets Using Tiled Display." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/CarterC2007.pdf.
Повний текст джерелаMemarsadeghi, Nargess. "Efficient algorithms for clustering and interpolation of large spatial data sets." College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/6839.
Повний текст джерелаThesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Sips, Mike. "Pixel-based visual data mining in large geo-spatial point sets /." Konstanz : Hartung-Gorre, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014881714&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Повний текст джерелаCoudret, Raphaël. "Stochastic modelling using large data sets : applications in ecology and genetics." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00865867.
Повний текст джерелаWinter, Eitan E. "Evolutionary analyses of protein-coding genes using large biological data sets." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427615.
Повний текст джерелаMostafa, Nour. "Intelligent dynamic caching for large data sets in a grid environment." Thesis, Queen's University Belfast, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.602689.
Повний текст джерелаKim, Hyeyoen. "Large data sets and nonlinearity : essays in international finance and macroeconomics." Thesis, University of Warwick, 2009. http://wrap.warwick.ac.uk/3747/.
Повний текст джерелаMellor, John Phillip 1965. "Automatically recovering geometry and texture from large sets of calibrated images." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/87157.
Повний текст джерелаIncludes bibliographical references (p. 129-135).
by J.P. Mellor.
Ph.D.
Deri, Joya A. "Graph Signal Processing: Structure and Scalability to Massive Data Sets." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/725.
Повний текст джерелаTowfeek, Ajden. "Multi-Resolution Volume Rendering of Large Medical Data Sets on the GPU." Thesis, Linköping University, Department of Science and Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10715.
Повний текст джерелаVolume rendering techniques can be powerful tools when visualizing medical data sets. The characteristics of being able to capture 3-D internal structures make the technique attractive. Scanning equipment is producing medical images, with rapidly increasing resolution, resulting in heavily increased size of the data set. Despite the great amount of processing power CPUs deliver, the required precision in image quality can be hard to obtain in real-time rendering. Therefore, it is highly desirable to optimize the rendering process.
Modern GPUs possess much more computational power and is available for general purpose programming through high level shading languages. Efficient representations of the data are crucial due to the limited memory provided by the GPU. This thesis describes the theoretical background and the implementation of an approach presented by Patric Ljung, Claes Lundström and Anders Ynnerman at Linköping University. The main objective is to implement a fully working multi-resolution framework with two separate pipelines for pre-processing and real-time rendering, which uses the GPU to visualize large medical data sets.
González, David Muñoz. "Discovering unknown equations that describe large data sets using genetic programming techniques." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2639.
Повний текст джерелаFIR filters are widely used nowadays, with applications from MP3 players, Hi-Fi systems, digital TVs, etc. to communication systems like wireless communication. They are implemented in DSPs and there are several trade-offs that make important to have an exact as possible estimation of the required filter order.
In order to find a better estimation of the filter order than the existing ones, genetic expression programming (GEP) is used. GEP is a Genetic Algorithm that can be used in function finding. It is implemented in a commercial application which, after the appropriate input file and settings have been provided, performs the evolution of the individuals in the input file so that a good solution is found. The thesis is the first one in this new research line.
The aim has been not only reaching the desired estimation but also pave the way for further investigations.
Bäckström, Daniel. "Managing and Exploring Large Data Sets Generated by Liquid Separation - Mass Spectrometry." Doctoral thesis, Uppsala University, Analytical Chemistry, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8223.
Повний текст джерелаA trend in natural science and especially in analytical chemistry is the increasing need for analysis of a large number of complex samples with low analyte concentrations. Biological samples (urine, blood, plasma, cerebral spinal fluid, tissue etc.) are often suitable for analysis with liquid separation mass spectrometry (LS-MS), resulting in two-way data tables (time vs. m/z). Such biological 'fingerprints' taken for all samples in a study correspond to a large amount of data. Detailed characterization requires a high sampling rate in combination with high mass resolution and wide mass range, which presents a challenge in data handling and exploration. This thesis describes methods for managing and exploring large data sets made up of such detailed 'fingerprints' (represented as data matrices).
The methods were implemented as scripts and functions in Matlab, a wide-spread environment for matrix manipulations. A single-file structure to hold the imported data facilitated both easy access and fast manipulation. Routines for baseline removal and noise reduction were intended to reduce the amount of data without loosing relevant information. A tool for visualizing and exploring single runs was also included. When comparing two or more 'fingerprints' they usually have to be aligned due to unintended shifts in analyte positions in time and m/z. A PCA-like multivariate method proved to be less sensitive to such shifts, and an ANOVA implementation made it easier to find systematic differences within the data sets.
The above strategies and methods were applied to complex samples such as plasma, protein digests, and urine. The field of application included urine profiling (paracetamole intake; beverage effects), peptide mapping (different digestion protocols) and search for potential biomarkers (appendicitis diagnosis) . The influence of the experimental factors was visualized by PCA score plots as well as clustering diagrams (dendrograms).
Cutchin, Andrew E. Donahoo Michael J. "Towards efficient and practical reliable bulk data transport for large receiver sets." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5140.
Повний текст джерелаHarrington, Justin. "Extending linear grouping analysis and robust estimators for very large data sets." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/845.
Повний текст джерелаKışınbay, Turgut. "Predictive ability or data snopping? : essays on forecasting with large data sets." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85018.
Повний текст джерелаDutta, Soumya. "In Situ Summarization and Visual Exploration of Large-scale Simulation Data Sets." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524070976058567.
Повний текст джерелаBlanc, Trevor Jon. "Analysis and Compression of Large CFD Data Sets Using Proper Orthogonal Decomposition." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/5303.
Повний текст джерелаNguyen, Minh Quoc. "Toward accurate and efficient outlier detection in high dimensional and large data sets." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34657.
Повний текст джерелаBresell, Anders. "Characterization of protein families, sequence patterns, and functional annotations in large data sets." Doctoral thesis, Linköping : Department of Physics, Chemistry and Biology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10565.
Повний текст джерелаCastro, Jose R. "MODIFICATIONS TO THE FUZZY-ARTMAP ALGORITHM FOR DISTRIBUTED LEARNING IN LARGE DATA SETS." Doctoral diss., University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4449.
Повний текст джерелаPh.D.
School of Electrical and Computer Engineering
Engineering and Computer Science
Electrical and Computer Engineering
Brind'Amour, Katherine. "Maternal and Child Health Home Visiting Evaluations Using Large, Pre-Existing Data Sets." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1468965739.
Повний текст джерелаQuiroz, Matias. "Bayesian Inference in Large Data Problems." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-118836.
Повний текст джерелаAt the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Submitted. Paper 2: Submitted. Paper 3: Manuscript. Paper 4: Manuscript.