Littérature scientifique sur le sujet « Search for the nearest neighbour »
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Articles de revues sur le sujet "Search for the nearest neighbour"
Myasnikov, E. « Exact Nearest Neighbour Search within Constrained Neighbourhood Using the Forest of Vp-Tree-Like Structures ». Journal of Physics : Conference Series 2096, no 1 (1 novembre 2021) : 012199. http://dx.doi.org/10.1088/1742-6596/2096/1/012199.
Texte intégralN, MINOJINI, GAYATHRI R. KRISHNA, REKHA A et SOWMIYAA P. « Dynamic Nearest Neighbour Search With Keywords ». IJARCCE 4, no 3 (30 mars 2015) : 580–82. http://dx.doi.org/10.17148/ijarcce.2015.43139.
Texte intégralSuhaibaha, A., A. A. Rahman, U. Uznir, F. Anton et D. Mioc. « IMPROVING NEAREST NEIGHBOUR SEARCH IN 3D SPATIAL ACCESS METHOD ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W1 (26 octobre 2016) : 69–73. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w1-69-2016.
Texte intégralHooda, Meenakshi, et Sumeet Gill. « Nearest Neighbour Search in k-dSLst Tree ». Advances in Science, Technology and Engineering Systems Journal 5, no 4 (juillet 2020) : 160–66. http://dx.doi.org/10.25046/aj050419.
Texte intégralBiswas, Sumana, Sreenatha G. Anavatti et Matthew A. Garratt. « A Time-Efficient Co-Operative Path Planning Model Combined with Task Assignment for Multi-Agent Systems ». Robotics 8, no 2 (26 avril 2019) : 35. http://dx.doi.org/10.3390/robotics8020035.
Texte intégralZhao, Ning, Jingyue Xu et Gang Zhou. « Fault Diagnosis of Centrifugal Fan Based on Grid Search Optimized Voting Weighted KNN ». Journal of Physics : Conference Series 2636, no 1 (1 novembre 2023) : 012046. http://dx.doi.org/10.1088/1742-6596/2636/1/012046.
Texte intégralSuhaibah, A., U. Uznir, F. Anton, D. Mioc et A. A. Rahman. « 3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (7 juin 2016) : 87–93. http://dx.doi.org/10.5194/isprs-archives-xli-b2-87-2016.
Texte intégralSuhaibah, A., U. Uznir, F. Anton, D. Mioc et A. A. Rahman. « 3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (7 juin 2016) : 87–93. http://dx.doi.org/10.5194/isprsarchives-xli-b2-87-2016.
Texte intégralCheung, King Lum, et Ada Wai-Chee Fu. « Enhanced nearest neighbour search on the R-tree ». ACM SIGMOD Record 27, no 3 (septembre 1998) : 16–21. http://dx.doi.org/10.1145/290593.290596.
Texte intégralAli, Mohammed Eunus, Saif-ul-Islam Khan, Sharowar Md Shahriar Khan et Md Nasim. « Spatio-temporal keyword search for nearest neighbour queries ». Journal of Location Based Services 9, no 2 (3 avril 2015) : 113–37. http://dx.doi.org/10.1080/17489725.2015.1066887.
Texte intégralThèses sur le sujet "Search for the nearest neighbour"
Kibriya, Ashraf Masood. « Fast Algorithms for Nearest Neighbour Search ». The University of Waikato, 2007. http://hdl.handle.net/10289/2463.
Texte intégralShehu, 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.
Texte intégralCasselryd, Oskar, et 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.
Texte intégralInternet-troll har de senaste åren fått ökat inflytande i och med ökat användande av sociala medier. En trollfarm är en grupp troll som får betalt för att sprida specifika åsikter eller information online. Det kan vara svårt att urskilja användarna i en trollfarm från vanliga användare då de ständigt försöker undvika upptäckt. I denna studie undersöks hurvida man kan finna en trollfarm på Twitter genom att utföra en sentimentanalys på användares tweets och sedan modelera det som ett nearest neighbor problem. Experimentet utfördes med 4 simulerade troll och 150 vanliga twitteranvändare. Användarna modelerades efter tid, frekvens och sentiment på deras tweets. Resultatet från modeleringen kunde inte påvisa ett samband mellan trollen då deras beteendemönster skiljde sig åt allt för mycket.
KUMAR, SUSMIT. « NEAREST NEIGHBOR SEARCH IN DISTRIBUTED DATABASES ». University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1022879916.
Texte intégralRam, Parikshit. « New paradigms for approximate nearest-neighbor search ». Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49112.
Texte intégralChanzy, Philippe. « Range search and nearest neighbor search in k-d trees ». Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68164.
Texte intégralMESEJO-LEON, DANIEL ALEJANDRO. « APPROXIMATE NEAREST NEIGHBOR SEARCH FOR THE KULLBACK-LEIBLER DIVERGENCE ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33305@1.
Texte intégralCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Em uma série de aplicações, os pontos de dados podem ser representados como distribuições de probabilidade. Por exemplo, os documentos podem ser representados como modelos de tópicos, as imagens podem ser representadas como histogramas e também a música pode ser representada como uma distribuição de probabilidade. Neste trabalho, abordamos o problema do Vizinho Próximo Aproximado onde os pontos são distribuições de probabilidade e a função de distância é a divergência de Kullback-Leibler (KL). Mostramos como acelerar as estruturas de dados existentes, como a Bregman Ball Tree, em teoria, colocando a divergência KL como um produto interno. No lado prático, investigamos o uso de duas técnicas de indexação muito populares: Índice Invertido e Locality Sensitive Hashing. Os experimentos realizados em 6 conjuntos de dados do mundo real mostraram que o Índice Invertido é melhor do que LSH e Bregman Ball Tree, em termos de consultas por segundo e precisão.
In a number of applications, data points can be represented as probability distributions. For instance, documents can be represented as topic models, images can be represented as histograms and also music can be represented as a probability distribution. In this work, we address the problem of the Approximate Nearest Neighbor where the points are probability distributions and the distance function is the Kullback-Leibler (KL) divergence. We show how to accelerate existing data structures such as the Bregman Ball Tree, by posing the KL divergence as an inner product embedding. On the practical side we investigated the use of two, very popular, indexing techniques: Inverted Index and Locality Sensitive Hashing. Experiments performed on 6 real world data-sets showed the Inverted Index performs better than LSH and Bregman Ball Tree, in terms of queries per second and precision.
Varricchio, Valerio. « Efficient nearest-neighbor search algorithms for sub-Riemannian geometries ». Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122500.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references.
The Motion Planning problem has been at the core of a significant amount of research in the past decades and it has recently gained traction outside academia with the rise of commercial interest in self-driving cars and autonomous aerial vehicles. Among the leading algorithms to tackle the problem are sampling-based planners, such as Probabilistic Road Maps (PRMs), Rapidly-exploring Random Trees (RRTs) and a large number of variants thereof. In this thesis, we focus on a crucial building block shared by these algorithms: nearest-neighbor search. While nearest-neighbor search is known as the asymptotically dominant bottleneck of sampling-based planners, popular algorithms to efficiently identify neighbors are limited to robots capable of unconstrained motions, commonly referred to as holonomic.
Nevertheless, this is rarely the case in the vast majority of practical applications, where the dynamical system at hand is often subject to a class of differential constraints called nonholonomic. We tackle the problem with sub-Riemannian geometries, a mathematical tool to study manifolds that can be traversed under local constraints. After drawing the parallel with nonholonomic mechanical systems, we exploit peculiar properties of these geometries and their natural notion of distance to devise specialized, efficient nearest-neighbor search algorithms. Our contributions are two-fold: First, we generalize existing space-partitioning techniques (k-d trees) to sub-Riemannian metrics. This is achieved by introducing i) a criterion - the outer Box Bound - that discards halfspaces consistently with the metric and ii) a space-partitioning technique - the Lie splitting strategy - that organizes the dataset for optimal asymptotic performance.
Second, we propose pruning techniques to further improve the query runtime. This is achieved by reducing the number of distance evaluations required to discern the nearest neighbors and exploiting heuristics that provably approximate a sub-Riemannian metric up to a constant factor, asymptotically.
by Valerio Varricchio.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
Zhang, Peiwu, et 张培武. « Voronoi-based nearest neighbor search for multi-dimensional uncertain databases ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49618179.
Texte intégralpublished_or_final_version
Computer Science
Master
Master of Philosophy
Andoni, Alexandr. « Nearest neighbor search : the old, the new, and the impossible ». Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55090.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (p. 165-178).
Over the last decade, an immense amount of data has become available. From collections of photos, to genetic data, and to network traffic statistics, modern technologies and cheap storage have made it possible to accumulate huge datasets. But how can we effectively use all this data? The ever growing sizes of the datasets make it imperative to design new algorithms capable of sifting through this data with extreme efficiency. A fundamental computational primitive for dealing with massive dataset is the Nearest Neighbor (NN) problem. In the NN problem, the goal is to preprocess a set of objects, so that later, given a query object, one can find efficiently the data object most similar to the query. This problem has a broad set of applications in data processing and analysis. For instance, it forms the basis of a widely used classification method in machine learning: to give a label for a new object, find the most similar labeled object and copy its label. Other applications include information retrieval, searching image databases, finding duplicate files and web pages, vector quantization, and many others. To represent the objects and the similarity measures, one often uses geometric notions. For example, a black-and-white image may be modeled by a high-dimensional vector, with one coordinate per pixel, whereas the similarity measure may be the standard Euclidean distance between the resulting vectors. Many other, more elaborate ways of representing objects by high-dimensional feature vectors have been studied. In this thesis, we study the NN problem, as well as other related problems that occur frequently when dealing with the massive datasets.
(cont.) Our contribution is two-fold: we significantly improve the algorithms within the classical approaches to NN, as well as propose new approaches where the classical ones fail. We focus on several key distances and similarity measures, including the Euclidean distance, string edit distance and the Earth-Mover Distance (a popular method for comparing images). We also give a number of impossibility results, pointing out the limits of the NN algorithms. The high-level structure of our thesis is summarized as follows. New algorithms via the classical approaches. We give a new algorithm for the approximate NN problem in the d-dimensional Euclidean space. For an approximation factor c > 1, our algorithm achieves dnP query time and dnl+P space for p = 1/c 2+o(1). This greatly improves on the previous algorithms that achieved p that was only slightly smaller than 1/c. The same technique also yields an algorithm with dno(p) query time and space near-linear in n. Furthermore, our algorithm is near-optimal in the class of "hashing" algorithms. Failure of the classical approaches for some hard distances. We give an evidence that the classical approaches to NN under certain hard distances, such as the string edit distance, meet a concrete barrier at a nearly logarithmic approximation. Specifically, we show that for all classical approaches to NN under the edit distance, involving embeddings into a general class of spaces (such as l1, powers of l2, etc), the resulting approximation has to be at least near-logarithmic in the strings' length. A new approach to NN under hard distances.
(cont.) Motivated by the above impossibility results, we develop a new approach to the NN problem, where the classical approaches fail. Using this approach, we give a new efficient NN algorithm for a variant of the edit distance, the Ulam distance, which achieves a double-logarithmic approximation. This is an exponential improvement over the lower bound on the approximation achievable via the previous classical approaches to this problem. Data structure lower bounds. To complement our algorithms, we prove lower bounds on NN data structures for the Euclidean distance and for the mysterious but important case of the ... distance. In both cases, our lower bounds are the first ones to hold in the same computational model as the respective upper bounds. Furthermore, for both problems, our lower bounds are optimal in the considered models. External applications. Although our main focus is on the NN problem, our techniques naturally extend to related problems. We give such applications for each of our algorithmic tools. For example, we give an algorithm for computing the edit distance between two strings of length d in near-linear time. Our algorithm achieves approximation 20 ..., improving over the previous bound of ... . We note that this problem has a classical exact algorithm based on dynamic programming, running in quadratic time.
by Alexandr Andoni.
Ph.D.
Livres sur le sujet "Search for the nearest neighbour"
Large Scale Nearest Neighbor Search - Theories, Algorithms, and Applications. [New York, N.Y.?] : [publisher not identified], 2014.
Trouver le texte intégralWeber, Roger. Similarity search in high dimensional vector spaces. Berlin : Aka, 2001.
Trouver le texte intégralBaşan, Ghillie. The moon's our nearest neighbour. London : Warner Books, 2001.
Trouver le texte intégralRoopchansingh, Ajay. Nearest neighbour interconnect architecture in deep-submicron FPGAs. Ottawa : National Library of Canada, 2002.
Trouver le texte intégralGwennyth, Zainu'ddin Ailsa, Australian Indonesian Association Victoria et Monash University. Centre of Southeast Asian Studies., dir. Nearest southern neighbour : Some Indonesian views of Australia and Australians. Clayton, Vic., Australia : Monash University, 1986.
Trouver le texte intégralGuo, Gongde. A study on the nearest neighbour method and its applications. [S.l : The Author], 2004.
Trouver le texte intégralBrandsma, Theo. Rainfall generator for the Rhine Basin : Single-site generation of weather variables by nearest-neighbour resampling. De Bilt, Netherlands : KNMI, 1997.
Trouver le texte intégralNearest Neighbor Search. Springer US, 2005. http://dx.doi.org/10.1007/0-387-27544-4.
Texte intégralPapadopoulos, Apostolos N., et Yannis Manolopoulos. Nearest Neighbor Search : : A Database Perspective. Springer London, Limited, 2006.
Trouver le texte intégralManolopoulos, Yannis, et Apostolos N. N. Papadopoulos. Nearest Neighbor Search : : A Database Perspective. Springer, 2010.
Trouver le texte intégralChapitres de livres sur le sujet "Search for the nearest neighbour"
Shekhar, Shashi, et Hui Xiong. « Nearest Neighbor Search ». Dans Encyclopedia of GIS, 783. Boston, MA : Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_867.
Texte intégralKhan, Omar Shahbaz, Martin Aumüller et Björn Þór Jónsson. « Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback ». Dans Similarity Search and Applications, 133–47. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46994-7_12.
Texte intégralSerrano, Aureo, Luisa Micó et Jose Oncina. « Which Fast Nearest Neighbour Search Algorithm to Use ? » Dans Pattern Recognition and Image Analysis, 567–74. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_67.
Texte intégralWeller, Frank, et Robert Mencl. « Nearest Neighbour Search for Visualization Using Arbitrary Triangulations ». Dans Eurographics, 191–200. Vienna : Springer Vienna, 1996. http://dx.doi.org/10.1007/978-3-7091-7488-3_20.
Texte intégralChappell, Timothy, Shlomo Geva et Guido Zuccon. « Approximate Nearest-Neighbour Search with Inverted Signature Slice Lists ». Dans Lecture Notes in Computer Science, 147–58. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16354-3_16.
Texte intégralGómez-Ballester, Eva, Luisa Micó et Jose Oncina. « Some Improvements in Tree Based Nearest Neighbour Search Algorithms ». Dans Lecture Notes in Computer Science, 456–63. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24586-5_56.
Texte intégralShaneck, Mark, Yongdae Kim et Vipin Kumar. « Privacy Preserving Nearest Neighbor Search ». Dans Machine Learning in Cyber Trust, 247–76. Boston, MA : Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-88735-7_10.
Texte intégralHruz, Tomas, et Marcel Schöngens. « Partially Specified Nearest Neighbor Search ». Dans Lecture Notes in Computer Science, 372–83. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32241-9_32.
Texte intégralKomorowski, Michał, et Tomasz Trzciński. « Random Binary Search Trees for Approximate Nearest Neighbour Search in Binary Space ». Dans Lecture Notes in Computer Science, 473–79. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69900-4_60.
Texte intégralWu, Xing, Geoffrey Holmes et Bernhard Pfahringer. « Mining Arbitrarily Large Datasets Using Heuristic k-Nearest Neighbour Search ». Dans AI 2008 : Advances in Artificial Intelligence, 355–61. Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89378-3_35.
Texte intégralActes de conférences sur le sujet "Search for the nearest neighbour"
Ferro, Demetrio, Vincent Gripon et Xiaoran Jiang. « Nearest Neighbour Search using binary neural networks ». Dans 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727873.
Texte intégralDick, Travis, Camilo Perez, Martin Jagersand et Azad Shademan. « Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search ». Dans Robotics : Science and Systems 2013. Robotics : Science and Systems Foundation, 2013. http://dx.doi.org/10.15607/rss.2013.ix.044.
Texte intégralStommel, Martin, Stefan Edelkamp, Thiemo Wiedemeyer et Michael Beetz. « Fractal Approximate Nearest Neighbour Search in Log-Log Time ». Dans British Machine Vision Conference 2013. British Machine Vision Association, 2013. http://dx.doi.org/10.5244/c.27.18.
Texte intégralVijay, Savinu T., et P. N. Pournami. « Feature Based Image Registration using Heuristic Nearest Neighbour Search ». Dans 2018 22nd International Computer Science and Engineering Conference (ICSEC). IEEE, 2018. http://dx.doi.org/10.1109/icsec.2018.8712669.
Texte intégralKurniawati, R., J. S. Jin et J. A. Shepherd. « An efficient nearest-neighbour search while varying Euclidean metrics ». Dans the sixth ACM international conference. New York, New York, USA : ACM Press, 1998. http://dx.doi.org/10.1145/290747.290812.
Texte intégralShao, Zhou, et David Taniar. « Range-based Nearest Neighbour Search in a Mobile Environment ». Dans MoMM '14 : The 12th International Conference on Advances in Mobile Computing and Multimedia. New York, NY, USA : ACM, 2014. http://dx.doi.org/10.1145/2684103.2684158.
Texte intégralChatterjee, Bapi, Ivan Walulya et Philippas Tsigas. « Concurrent Linearizable Nearest Neighbour Search in LockFree-kD-tree ». Dans ICDCN '18 : 19th International Conference on Distributed Computing and Networking. New York, NY, USA : ACM, 2018. http://dx.doi.org/10.1145/3154273.3154307.
Texte intégralMoreno-Seco, F., L. Mico et J. Oncina. « A new classification rule based on nearest neighbour search ». Dans Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333789.
Texte intégralTellez, Eric Sadit, Edgar Chávez et Gonzalo Navarro. « Succinct nearest neighbor search ». Dans the Fourth International Conference. New York, New York, USA : ACM Press, 2011. http://dx.doi.org/10.1145/1995412.1995420.
Texte intégral« Grid-based Spatial Index Method for Location-based Nearest Neighbour Search ». Dans 2019 the 9th International Workshop on Computer Science and Engineering. WCSE, 2019. http://dx.doi.org/10.18178/wcse.2019.03.030.
Texte intégralRapports d'organisations sur le sujet "Search for the nearest neighbour"
Gonzales, Antonio, et Nicholas Paul Blazier. Enhanced Approximate Nearest Neighbor via Local Area Focused Search. Office of Scientific and Technical Information (OSTI), février 2017. http://dx.doi.org/10.2172/1367491.
Texte intégralMackey, Greg Edward. Efficient nearest neighbor searches in N-ABLE. Office of Scientific and Technical Information (OSTI), juillet 2010. http://dx.doi.org/10.2172/992313.
Texte intégral