Relevant bibliographies by topics / Large sets

Academic literature on the topic 'Large sets'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Large sets"

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Halmos, Paul R. "Large Intersections of Large Sets." American Mathematical Monthly 99, no. 4 (April 1992): 307. http://dx.doi.org/10.2307/2324896.

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Halmos, Paul R. "Large Intersections of Large Sets." American Mathematical Monthly 99, no. 4 (April 1992): 307–12. http://dx.doi.org/10.1080/00029890.1992.11995853.

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Komjáth, Péter. "Large small sets." Colloquium Mathematicum 56, no. 2 (1988): 231–33. http://dx.doi.org/10.4064/cm-56-2-231-233.

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Jervell, Herman Ruge. "Large Finite Sets." Zeitschrift für Mathematische Logik und Grundlagen der Mathematik 31, no. 35-36 (1985): 545–49. http://dx.doi.org/10.1002/malq.19850313502.

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Jasinski. "LARGE SETS CONTAINING COPIES OF SMALL SETS." Real Analysis Exchange 21, no. 2 (1995): 758. http://dx.doi.org/10.2307/44152689.

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Etzion, Tuvi, and Junling Zhou. "Large sets with multiplicity." Designs, Codes and Cryptography 89, no. 7 (May 20, 2021): 1661–90. http://dx.doi.org/10.1007/s10623-021-00878-4.

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Fraser, Robert, and Malabika Pramanik. "Large sets avoiding patterns." Analysis & PDE 11, no. 5 (April 11, 2018): 1083–111. http://dx.doi.org/10.2140/apde.2018.11.1083.

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Miller, DR, and DJ Quammen. "Exploiting large register sets." Microprocessors and Microsystems 14, no. 6 (July 1990): 333–40. http://dx.doi.org/10.1016/0141-9331(90)90105-5.

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Teirlinck, Luc. "Large sets with holes." Journal of Combinatorial Designs 1, no. 1 (1993): 69–94. http://dx.doi.org/10.1002/jcd.3180010108.

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Etzion, Tuvi. "Large sets of coverings." Journal of Combinatorial Designs 2, no. 5 (1994): 359–74. http://dx.doi.org/10.1002/jcd.3180020509.

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Dissertations / Theses on the topic "Large sets"

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Ziegler, Albert. "Large sets in constructive set theory." Thesis, University of Leeds, 2014. http://etheses.whiterose.ac.uk/8370/.

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This thesis presents an investigation into large sets and large set axioms in the context of the constructive set theory CZF. We determine the structure of large sets by classifying their von Neumann stages and use a new modified cumulative hierarchy to characterise their arrangement in the set theoretic universe. We prove that large set axioms have good metamathematical properties, including absoluteness for the common relative model constructions of CZF and a preservation of the witness existence properties CZF enjoys. Furthermore, we use realizability to establish new results about the relative consistency of a plurality of inaccessibles versus the existence of just one inaccessible. Developing a constructive theory of clubs, we present a characterisation theorem for Mahlo sets connecting classical and constructive approaches to Mahloness and determine the amount of induction contained in the assertion of a Mahlo set. We then present a characterisation theorem for 2-strong sets which proves them to be equivalent to a logically simpler concept. We also investigate several topics connected to elementary embeddings of the set theoretic universe into a transitive class model of CZF, where considering different equivalent classical formulations results in a rich and interconnected spectrum of measurability for the constructive case. We pay particular attention to the question of cofinality of elementary embeddings, achieving both very strong cofinality properties in the case of Reinhardt embeddings and constructing models of the failure of cofinality in the case of ordinary measurable embeddings, some of which require only surprisingly low conditions. We close with an investigation of constructive principles incompatible with elementary embeddings.
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Kleinberg, Robert David. "Online decision problems with large strategy sets." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33092.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2005.
Includes 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.
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Villalba, Michael Joseph. "Fast visual recognition of large object sets." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/42211.

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Arvidsson, 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.

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To find out what differs between two versions of a file can be done with several different techniques and programs. These techniques and programs are often focusd on finding differences in text files, in documents, or in class files for programming. An example of a program is the popular git tool which focuses on displaying the difference between versions of files in a project. A common way to find these differences is to utilize an algorithm called Longest common subsequence, which focuses on finding the longest common subsequence in each file to find similarity between the files. By excluding all similarities in a file, all remaining text will be the differences between the files. The Longest Common Subsequence is often used to find the differences in an acceptable time. When two lines in a file is compared to see if they differ from each other hashing is used. The hash values for each correspondent line in both files will be compared. Hashing a line will give the content on that line a unique value. If as little as one character on a line is different between the version, the hash values for those lines will be different as well. These techniques are very useful when comparing two versions of a file with text content. With data from a database some, but not all, of these techniques can be useful. A big difference between data in a database and text in a file will be that content is not just added and delete but also updated. This thesis studies the problem on how to make use of these techniques when finding differences between large datasets, and doing this in a reasonable time, instead of finding differences in documents and files.  Three different methods are going to be studied in theory. These results will be provided in both time and space complexities. Finally, a selected one of these methods is further studied with implementation and testing. The reason only one of these three is implemented is because of time constraint. The one that got chosen had easy maintainability, an easy implementation, and maintains a good execution time.
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Bate, Steven Mark. "Generalized linear models for large dependent data sets." Thesis, University College London (University of London), 2004. http://discovery.ucl.ac.uk/1446542/.

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Generalized linear models (GLMs) were originally used to build regression models for independent responses. In recent years, however, effort has focused on extending the original GLM theory to enable it to be applied to data which exhibit dependence in the responses. This thesis focuses on some specific extensions of the GLM theory for dependent responses. A new hypothesis testing technique is proposed for the application of GLMs to cluster dependent data. The test is based on an adjustment to the 'independence' likelihood ratio test, which allows for the within cluster dependence. The performance of the new test, in comparison to established techniques, is explored. The application of the generalized estimating equations (GEE) methodology to model space-time data is also investigated. The approach allows for the temporal dependence via the covariates and models the spatial dependence using techniques from geostatistics. The application area of climatology has been used to motivate much of the work undertaken. A key attribute of climate data sets, in addition to exhibiting dependence both spatially and temporally, is that they are typically large in size, often running into millions of observations. Therefore, throughout the thesis, particular attention has focused on computational issues, to enable analysis to be undertaken in a feasible time frame. For example, we investigate the use of the GEE one-step estimator in situations where the application of the full algorithm is impractical. The final chapter of this thesis presents a climate case study. This involves wind speeds over northwestern Europe, which we analyse using the techniques developed.
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Cordeiro, 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/.

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Due to the increasing amount and complexity of the data stored in the enterprises\' databases, the task of knowledge discovery is nowadays vital to support strategic decisions. However, the mining techniques used in the process usually have high computational costs that come from the need to explore several alternative solutions, in different combinations, to obtain the desired knowledge. The most common mining tasks include data classification, labeling and clustering, outlier detection and missing data prediction. Traditionally, the data are represented by numerical or categorical attributes in a table that describes one element in each tuple. Although the same tasks applied to traditional data are also necessary for more complex data, such as images, graphs, audio and long texts, the complexity and the computational costs associated to handling large amounts of these complex data increase considerably, making most of the existing techniques impractical. Therefore, especial data mining techniques for this kind of data need to be developed. This Ph.D. work focuses on the development of new data mining techniques for large sets of complex data, especially for the task of clustering, tightly associated to other data mining tasks that are performed together. Specifically, this Doctoral dissertation presents three novel, fast and scalable data mining algorithms well-suited to analyze large sets of complex data: the method Halite for correlation clustering; the method BoW for clustering Terabyte-scale datasets; and the method QMAS for labeling and summarization. Our algorithms were evaluated on real, very large datasets with up to billions of complex elements, and they always presented highly accurate results, being at least one order of magnitude faster than the fastest related works in almost all cases. The real data used come from the following applications: automatic breast cancer diagnosis, satellite imagery analysis, and graph mining on a large web graph crawled by Yahoo! and also on the graph with all users and their connections from the Twitter social network. Such results indicate that our algorithms allow the development of real time applications that, potentially, could not be developed without this Ph.D. work, like a software to aid on the fly the diagnosis process in a worldwide Healthcare Information System, or a system to look for deforestation within the Amazon Rainforest in real time
O 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
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Chaudhary, Amitabh. "Applied spatial data structures for large data sets." Available to US Hopkins community, 2002. http://wwwlib.umi.com/dissertations/dlnow/3068131.

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Hennessey, Anthony. "Statistical shape analysis of large molecular data sets." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52088/.

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Protein classification databases are widely used in the prediction of protein structure and function, and amongst these databases the manually-curated Structural Classification of Proteins database (SCOP) is considered to be a gold standard. In SCOP, functional relationships are described by hyperfamily and superfamily categories and structural relationships are described by family, species and protein categories. We present a method to calculate a difference measure between pairs of proteins that can be used to reproduce SCOP2 structural relationship classifications, and that can also be used to reproduce a subset of functional relationship classifications at the superfamily level. Calculating the difference measure requires first finding the best correspondence between atoms in two protein configurations. The problem of finding the best correspondence is known as the unlabelled, partial matching problem. We consider the unlabelled, partial matching problem through a detailed analysis of the approach presented in Green and Mardia (2006). Using this analysis, and applying domain-specific constraints, we develop a new algorithm called GProtA for protein structure alignment. The proposed difference measure is constructed from the root mean squared deviation of the aligned protein structures and a binary similarity measure, where the binary similarity measure takes into account the proportions of atoms matching from each configuration. The GProtA algorithm and difference measure are applied to protein structure data taken from the Protein Data Bank. The difference measure is shown to correctly classify 62 of a set of 72 proteins into the correct SCOP family categories when clustered. Of the remaining 9 proteins, 2 are assigned incorrectly and 7 are considered indeterminate. In addition, a method for deriving characteristic signatures for categories is proposed. The signatures offer a mechanism by which a single comparison can be made to judge similarity to a particular category. Comparison using characteristic signatures is shown to correctly delineate proteins at the family level, including the identification of both families for a subset of proteins described by two family level categories.
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Huet, 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.

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Thorarinsson, 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.

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Event 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.

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Books on the topic "Large sets"

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P, Keenan Maryanne, and United States. Agency for Health Care Policy and Research., eds. Measuring cognitive impairment with large data sets. Rockville, MD (18-12 Parklawn Bldg., Rockville 20857): U.S. Dept. of Health and Human Services, Public Health Service, Agency for Health Care Policy and Research, 1990.

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1973-, Wang Wei, and Yang Jiong, eds. Mining sequential patterns from large data sets. New York: Springer, 2005.

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Stock, James H. Estimating turning points using large data sets. Cambridge, MA: National Bureau of Economic Research, 2010.

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Murder sets seed. London: Robert Hale, 2001.

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Murder sets seed. New York: St. Martin's Minotaur, 2000.

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Murder sets seed. Thorndike, Me: Thorndike Press, 2001.

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Qingde, Kang. Large sets of triple systems and related designs. Elmhurst, NY: Science Press New York, 1995.

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Cordeiro, Robson L. F., Christos Faloutsos, and Caetano Traina Júnior. Data Mining in Large Sets of Complex Data. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4890-6.

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Cordeiro, Robson L. F. Data Mining in Large Sets of Complex Data. London: Springer London, 2013.

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Di Ciaccio, Agostino, Mauro Coli, and Jose Miguel Angulo Ibanez, eds. Advanced Statistical Methods for the Analysis of Large Data-Sets. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-21037-2.

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Book chapters on the topic "Large sets"

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Goldblatt, Robert. "Large Sets." In Graduate Texts in Mathematics, 15–21. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0615-6_2.

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Shoenfield, Joseph R. "Large RE Sets." In Lecture Notes in Logic, 63–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-22378-9_17.

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Meyer, Yves François. "A Note on Harmonious Sets." In Analysis at Large, 363–71. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05331-3_15.

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Barvinok, Alexander. "Convex sets at large." In A Course in Convexity, 1–39. Providence, Rhode Island: American Mathematical Society, 2002. http://dx.doi.org/10.1090/gsm/054/01.

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Schwartz, J. T., R. B. K. Dewar, E. Schonberg, and E. Dubinsky. "Structuring Large SETL Programs." In Programming with Sets, 323–44. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-9575-1_8.

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Taylor, Robert L., and Hiroshi Inoue. "Laws of Large Numbers for Random Sets." In Random Sets, 347–60. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1942-2_15.

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Goncharova, Elena, and Alexander Ovseevich. "Asymptotics for Singularly Perturbed Reachable Sets." In Large-Scale Scientific Computing, 280–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12535-5_32.

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Meitiner, Philip, and Pradeeka Seneviratne. "Working with Large Data Sets." In Beginning Data Science, IoT, and AI on Single Board Computers, 79–103. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5766-1_4.

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Johnson, Theodore, and Damianos Chatziantoniou. "Joining Very Large Data Sets." In Databases in Telecommunications, 118–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10721056_9.

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Jin, Peiquan. "Structures for Large Data Sets." In Encyclopedia of Big Data Technologies, 1–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_168-1.

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Conference papers on the topic "Large sets"

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Simas, Tiago, Gabriel Silva, Bruno Miranda, Andre Moitinho, Rita Ribeiro, and Coryn A. L. Bailer-Jones. "Knowledge Discovery in Large Data Sets." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059044.

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Van Gool, Luc, Michael D. Breitenstein, Stephan Gammeter, Helmut Grabner, and Till Quack. "Mining from large image sets." In Proceeding of the ACM International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1646396.1646410.

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"Session MA6b: Large data sets." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421089.

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Schotland, John C. "Optical Tomography with Large Data Sets." In Frontiers in Optics. Washington, D.C.: OSA, 2006. http://dx.doi.org/10.1364/fio.2006.fwv2.

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Mazlack, Lawrence J. "Imprecise causality in large data sets." In NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2008. http://dx.doi.org/10.1109/nafips.2008.4531206.

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Schotland, John C. "Optical Tomography with Large Data Sets." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2007. http://dx.doi.org/10.1364/cosi.2007.ctub1.

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Grohe, Martin, André Hernich, and Nicole Schweikardt. "Randomized computations on large data sets." In the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1142351.1142387.

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Rosenberg, Robert O., Marco O. Lanzagorta, Almadena Chtchelkanova, and Alexei Khokhlov. "Parallel visualization of large data sets." In Electronic Imaging, edited by Robert F. Erbacher, Philip C. Chen, Jonathan C. Roberts, and Craig M. Wittenbrink. SPIE, 2000. http://dx.doi.org/10.1117/12.378889.

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Vega, Juan Jaime, Humberto Carrillo-Calvet, and José Luis Jiménez-Andrade. "Regularization methods vs large training sets." In Artificial Intelligence for Science, Industry and Society. Trieste, Italy: Sissa Medialab, 2020. http://dx.doi.org/10.22323/1.372.0028.

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HŮLA, JAN, and IRINA PERFILIEVA. "LEARNING FROM LARGE SYNTHETIC DATA SETS." In Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016). WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146976_0055.

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Reports on the topic "Large sets"

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Stock, James, and Mark Watson. Estimating Turning Points Using Large Data Sets. Cambridge, MA: National Bureau of Economic Research, November 2010. http://dx.doi.org/10.3386/w16532.

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DeVore, Ronald A., Peter G. Binev, and Robert C. Sharpley. Advanced Mathematical Methods for Processing Large Data Sets. Fort Belvoir, VA: Defense Technical Information Center, October 2008. http://dx.doi.org/10.21236/ada499985.

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Gertz, E. M., and J. D. Griffin. Support vector machine classifiers for large data sets. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/881587.

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Hammond, William E., Vivian West, David Borland, Igor Akushevich, and Eugenia M. Heinz. Novel Visualization of Large Health Related Data Sets. Fort Belvoir, VA: Defense Technical Information Center, March 2014. http://dx.doi.org/10.21236/ada614184.

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Hammond, William E., Vivian L. West, David Borland, Igor Akushevich, and Eugenia M. Heinz. Novel Visualization of Large Health Related Data Sets. Fort Belvoir, VA: Defense Technical Information Center, March 2015. http://dx.doi.org/10.21236/ada624744.

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Carr, D. B. Looking at large data sets using binned data plots. Office of Scientific and Technical Information (OSTI), April 1990. http://dx.doi.org/10.2172/6930282.

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Hammond, William E., Vivian West, David Borland, Igor Akushevich, and Eugenia M. Heinz. Novel Visualization of Large Health Related Data Sets - NPHRD. Fort Belvoir, VA: Defense Technical Information Center, November 2015. http://dx.doi.org/10.21236/ada624632.

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Hodson, Stephen W., Stephen W. Poole, Thomas Ruwart, and Bradley W. Settlemyer. Moving Large Data Sets Over High-Performance Long Distance Networks. Office of Scientific and Technical Information (OSTI), April 2011. http://dx.doi.org/10.2172/1016604.

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Hurley, Michael B., and Edward K. Kao. Numerical Estimation of Information Theoretic Measures for Large Data Sets. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada580524.

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10

Thomas, Maikel A., Heidi Anne Smartt, and Robert F. Matthews. Processing large sensor data sets for safeguards : the knowledge generation system. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1039393.

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