Academic literature on the topic 'Distance vectors'
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Journal articles on the topic "Distance vectors"
Guo, Li, Guo Feng Liu, and Yu E. Bao. "A Study of FCM Clustering Algorithm Based on Interval Multiple Attribute Information." Applied Mechanics and Materials 444-445 (October 2013): 676–80. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.676.
Full textWu, Shen Hui, Sridhar Jandhyala, Yashwant K. Malaiya, and Anura P. Jayasumana. "Antirandom Testing: A Distance-Based Approach." VLSI Design 2008 (March 17, 2008): 1–9. http://dx.doi.org/10.1155/2008/165709.
Full textYi, Jing, Xue Dong Li, Xiao Feng Li, and Hong Ling Gou. "Research on P2P Transmission Optimizing Strategy." Applied Mechanics and Materials 513-517 (February 2014): 1797–802. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1797.
Full textLei, Lei, and She Kun. "Speaker Recognition Using Wavelet Packet Entropy, I-Vector, and Cosine Distance Scoring." Journal of Electrical and Computer Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1735698.
Full textDrakakis, Konstantinos, Roderick Gow, and Scott Rickard. "Common distance vectors between Costas arrays." Advances in Mathematics of Communications 3, no. 1 (2009): 35–52. http://dx.doi.org/10.3934/amc.2009.3.35.
Full textOZTURK, OZGUR, and HAKAN FERHATOSMANOGLU. "VECTOR SPACE INDEXING FOR BIOSEQUENCE SIMILARITY SEARCHES." International Journal on Artificial Intelligence Tools 14, no. 05 (October 2005): 811–26. http://dx.doi.org/10.1142/s0218213005002405.
Full textQian, Yan, Chun Guang Bai, Bai Quan Chen, and Gui Jin Mu. "Research on the Factors of Wind Direction and Distance in the Impact of Sand Source on its Neighborhood: An Example from Three Regions of Southern Xinjiang, China." Applied Mechanics and Materials 507 (January 2014): 845–50. http://dx.doi.org/10.4028/www.scientific.net/amm.507.845.
Full textLin, Jyh-Shyan, Jen-Chun Chang, Rong-Jaye Chen, and Torleiv Klove. "Distance-Preserving and Distance-Increasing Mappings From Ternary Vectors to Permutations." IEEE Transactions on Information Theory 54, no. 3 (March 2008): 1334–39. http://dx.doi.org/10.1109/tit.2007.915706.
Full textMatsui, Muneya, Thomas Mikosch, and Gennady Samorodnitsky. "Distance covariance for stochastic processes." Probability and Mathematical Statistics 37, no. 2 (May 14, 2018): 355–72. http://dx.doi.org/10.19195/0208-4147.37.2.9.
Full textZhang, Peng, Xiaogang Wang, and Peter X. K. Song. "Clustering Categorical Data Based on Distance Vectors." Journal of the American Statistical Association 101, no. 473 (March 2006): 355–67. http://dx.doi.org/10.1198/016214505000000312.
Full textDissertations / Theses on the topic "Distance vectors"
Shaik, Abdul Ameer Basha. "SVM Classification and Analysis of Margin Distance on Microarray Data." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924.
Full textHjelm, Andersson Patrick. "Binär matchning av bilder med hjälp av vektorer från deneuklidiska avståndstransformen." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2440.
Full textThis thesis shows the result from investigations of methods that use distance vectors when matching pictures. The distance vectors are available in a distance map made by the Euclidean Distance Transform. The investigated methods use the two characteristic features of the distance vector when matching pictures, length and direction. The length of the vector is used to calculate a value of how good a match is and the direction of the vector is used to predict a transformation to get a better match. The results shows that the number of calculation steps that are used during a search can be reduced compared to matching methods that only uses the distance during the matching.
Visa, Sofia. "Comparative Study of Methods for Linguistic Modeling of Numerical Data." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1043254774.
Full textThomson, R. G. W. "Vector acoustic miss distance indication." Master's thesis, University of Cape Town, 1985. http://hdl.handle.net/11427/21868.
Full textGirard, Jean-François. "Failure tolerant alternate-path distance-vector routing." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61200.
Full textFang, Xiao Ph D. Massachusetts Institute of Technology. "Bayesian distance metric learning on i-vector for speaker verification." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84870.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 63-66).
This thesis explores the use of Bayesian distance metric learning (Bayes_dml) for the task of speaker verification using the i-vector feature representation. We propose a framework that explores the distance constraints between i-vector pairs from the same speaker and different speakers. With an approximation of the distance metric as a weighted covariance matrix of the top eigenvectors from the data covariance matrix, variational inference is used to estimate a posterior distribution of the distance metric. Given speaker labels, we select different-speaker data pairs with the highest cosine scores to form a different-speaker constraint set. This set captures the most discriminative between-speaker variability that exists in the training data. This system is evaluated on the female part of the 2008 NIST SRE dataset. Cosine similarity scoring, as the state-of-the-art approach, is compared to Bayes-dml. Experimental results show the comparable performance between Bayes_dml and cosine similarity scoring. Furthermore, Bayes-dml is insensitive to score normalization, as compared to cosine similarity scoring. Without the requirement of the number of labeled examples, Bayes_dml performs better in the context of limited training data
by Xiao Fang.
S.M.
Miško, Lukáš. "Optimalizace směrování v protokolu Ad hoc On-Demand Distance Vector." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413102.
Full textEerla, Vishwa Shanthi. "Multi-Criteria Mapping Based on Support Vector Machine and Cluster Distance." Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-212565.
Full textDu, Plessis Johan. "ACODV : Ant Colony Optimisation Distance Vector routing in ad hoc networks." Diss., University of Pretoria, 2005. http://hdl.handle.net/2263/23923.
Full textDissertation (MSc)--University of Pretoria, 2005.
Computer Science
Unrestricted
Trhlík, Jiří. "Modelování distance-vektor směrovacích protokolů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-235460.
Full textBooks on the topic "Distance vectors"
Islei, Gerd. An approach to preference vector derivation using least square distance. Manchester: Manchester Business School, 1986.
Find full textIslei, Gerd. An approach to measuring consistency of preference vector derivations using least square distance. Manchester: Manchester Business School, 1986.
Find full textWittman, David M. Spacetime Geometry. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199658633.003.0011.
Full textIP Routing Simulator: Distance Vector Protocols. Macmillan Computer Publishing (a Pearson Education company), 2000.
Find full textCisco, Systems Inc. CISCO Interactive Mentor IP Routing: Distance-Vector Protocols (With CD-ROM). Cisco Press, 2000.
Find full textHrushovski, Ehud, and François Loeser. The space of stably dominated types. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691161686.003.0003.
Full textQoS Analysis of AdHoc On-Demand Distance Vector Routing Protocol: Theory and Experimental Analysis by TigerHATS. Germany: LAP Lambert Academic Publishing, 2012.
Find full textSimulation and Performance Analysis of the AD HOC On-Demand Distance Vector Routing Protocol for Tactical Mobile AD HOC Networks. Storming Media, 2000.
Find full textMann, Peter. Energy and Work. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822370.003.0002.
Full textGozlan, Rodolphe E., and Marine Combe. Environmental change and pathogen transmission. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789833.003.0005.
Full textBook chapters on the topic "Distance vectors"
Ekinci, Murat, and Eyup Gedikli. "Gait Recognition Using View Distance Vectors." In Computational Intelligence and Security, 973–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596448_144.
Full textAharonov, Yakir, and Lev Vaidman. "Protective Measurements of Two-State Vectors." In Potentiality, Entanglement and Passion-at-a-Distance, 1–8. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-2732-7_1.
Full textLyubashevsky, Vadim, and Daniele Micciancio. "On Bounded Distance Decoding, Unique Shortest Vectors, and the Minimum Distance Problem." In Advances in Cryptology - CRYPTO 2009, 577–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03356-8_34.
Full textNaor, Assaf, and Krzysztof Oleszkiewicz. "Moments of the Distance Between Independent Random Vectors." In Lecture Notes in Mathematics, 229–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46762-3_10.
Full textLi, Baoqing. "Distance-Based Selection of Potential Support Vectors by Kernel Matrix." In Advances in Neural Networks – ISNN 2004, 468–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28647-9_78.
Full textNi, Tongguang, Zongyuan Ding, Fuhua Chen, and Hongyuan Wang. "Relative Distance Metric Leaning Based on Clustering Centralization and Projection Vectors Learning for Person Re-identification." In Communications in Computer and Information Science, 482–93. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7299-4_40.
Full textNielsen, B., F. Albregtsen, and H. E. Danielsen. "Fractal Signature Vectors and Lacunarity Class Distance Matrices to Extract New Adaptive Texture Features from Cell Nuclei." In Fractals in Biology and Medicine, 55–65. Basel: Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8119-7_6.
Full textBielecki, Włodzimierz, Krzysztof Kraska, and Tomasz Klimek. "Using Basis Dependence Distance Vectors to Calculate the Transitive Closure of Dependence Relations by Means of the Floyd-Warshall Algorithm." In Combinatorial Optimization and Applications, 129–40. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03780-6_12.
Full textAmin, Kaizar A., John T. Mayes, and Armin R. Mikler. "Agent-Based Distance Vector Routing." In Lecture Notes in Computer Science, 41–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44651-6_5.
Full textCarrizosa, Emilio. "Support vector machines and distance minimization." In CRM Proceedings and Lecture Notes, 1–13. Providence, Rhode Island: American Mathematical Society, 2008. http://dx.doi.org/10.1090/crmp/045/01.
Full textConference papers on the topic "Distance vectors"
Drakakis, Konstantinos, Rod Gow, and Scott Rickard. "Distance vectors in Costas arrays." In 2008 42nd Annual Conference on Information Sciences and Systems (CISS). IEEE, 2008. http://dx.doi.org/10.1109/ciss.2008.4558707.
Full textChang, Jen-Chun, and Hsin-Lung Wu. "Distance-increasing mappings from binary vectors to constant composition vectors." In 2009 IEEE International Symposium on Information Theory - ISIT. IEEE, 2009. http://dx.doi.org/10.1109/isit.2009.5206023.
Full textNiwa, Yoshiki, and Yoshihiko Nitta. "Co-occurrence vectors from corpora vs. distance vectors from dictionaries." In the 15th conference. Morristown, NJ, USA: Association for Computational Linguistics, 1994. http://dx.doi.org/10.3115/991886.991938.
Full textChang, J. C., R. J. Chen, and S. C. Tsai. "Distance preserving mappings from binary vectors to permutations." In IEEE International Symposium on Information Theory, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/isit.2003.1228028.
Full textNurges, U., and E. Rustern. "The distance from stability boundary and reflection vectors." In Proceedings of 2002 American Control Conference. IEEE, 2002. http://dx.doi.org/10.1109/acc.2002.1024539.
Full textKuo, Huang-Cheng, Pei-Yuan Jou, and Jen-Peng Huang. "Adaptive Weighting Distance for Feature Vectors of Biological Sequences." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370523.
Full textYaqin, Guo, and Wang Zhengqun. "Support vectors pre-extracting method based on boundary distance." In 2016 IEEE International Conference on Mechatronics and Automation. IEEE, 2016. http://dx.doi.org/10.1109/icma.2016.7558876.
Full textSooksatra, Sorn, Toshiaki Kondo, and Pished Bunnun. "A drowsiness detection method using distance and gradient vectors." In 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES). IEEE, 2015. http://dx.doi.org/10.1109/ictemsys.2015.7110812.
Full textCicalese, Ferdinando, Luisa Gargano, and Ugo Vaccaro. "Approximating probability distributions with short vectors, via information theoretic distance measures." In 2016 IEEE International Symposium on Information Theory (ISIT). IEEE, 2016. http://dx.doi.org/10.1109/isit.2016.7541477.
Full textSooksatra, Sorn, Toshiaki Kondo, and Pished Bunnun. "A robust method for drowsiness detection using distance and gradient vectors." In 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2015. http://dx.doi.org/10.1109/ecticon.2015.7206977.
Full textReports on the topic "Distance vectors"
Waitzman, D., C. Partridge, and S. E. Deering. Distance Vector Multicast Routing Protocol. RFC Editor, November 1988. http://dx.doi.org/10.17487/rfc1075.
Full textPerkins, C., E. Belding-Royer, and S. Das. Ad hoc On-Demand Distance Vector (AODV) Routing. RFC Editor, July 2003. http://dx.doi.org/10.17487/rfc3561.
Full textLinn, Robert L., Stephen B. Dunbar, and Eva L. Baker. Psychometric Issues Related to the Five-Vector Model: Knowledge, Models and Tools to Improve the Effectiveness of Naval Distance Learning. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada450134.
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