Literatura académica sobre el tema "Data approximation"
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Artículos de revistas sobre el tema "Data approximation"
FROYLAND, GARY, KEVIN JUDD, ALISTAIR I. MEES, DAVID WATSON y KENJI MURAO. "CONSTRUCTING INVARIANT MEASURES FROM DATA". International Journal of Bifurcation and Chaos 05, n.º 04 (agosto de 1995): 1181–92. http://dx.doi.org/10.1142/s0218127495000843.
Texto completoGrubas, Serafim I., Georgy N. Loginov y Anton A. Duchkov. "Traveltime-table compression using artificial neural networks for Kirchhoff-migration processing of microseismic data". GEOPHYSICS 85, n.º 5 (19 de agosto de 2020): U121—U128. http://dx.doi.org/10.1190/geo2019-0427.1.
Texto completoSTOJANOVIĆ, MIRJANA. "PERTURBED SCHRÖDINGER EQUATION WITH SINGULAR POTENTIAL AND INITIAL DATA". Communications in Contemporary Mathematics 08, n.º 04 (agosto de 2006): 433–52. http://dx.doi.org/10.1142/s0219199706002180.
Texto completoFRAHLING, GEREON, PIOTR INDYK y CHRISTIAN SOHLER. "SAMPLING IN DYNAMIC DATA STREAMS AND APPLICATIONS". International Journal of Computational Geometry & Applications 18, n.º 01n02 (abril de 2008): 3–28. http://dx.doi.org/10.1142/s0218195908002520.
Texto completoChen, Jing-Bo, Hong Liu y Zhi-Fu Zhang. "A separable-kernel decomposition method for approximating the DSR continuation operator". GEOPHYSICS 72, n.º 1 (enero de 2007): S25—S31. http://dx.doi.org/10.1190/1.2399368.
Texto completoMardia, K. V. y I. L. Dryden. "Shape distributions for landmark data". Advances in Applied Probability 21, n.º 4 (diciembre de 1989): 742–55. http://dx.doi.org/10.2307/1427764.
Texto completoMardia, K. V. y I. L. Dryden. "Shape distributions for landmark data". Advances in Applied Probability 21, n.º 04 (diciembre de 1989): 742–55. http://dx.doi.org/10.1017/s0001867800019029.
Texto completoBirch, A. C. y A. G. Kosovichev. "Towards a Wave Theory Interpretation of Time-Distance Helioseismology Data". Symposium - International Astronomical Union 203 (2001): 180–82. http://dx.doi.org/10.1017/s0074180900219025.
Texto completoDong, Bin, Zuowei Shen y Jianbin Yang. "Approximation from Noisy Data". SIAM Journal on Numerical Analysis 59, n.º 5 (enero de 2021): 2722–45. http://dx.doi.org/10.1137/20m1389091.
Texto completoPiegl, L. A. y W. Tiller. "Data Approximation Using Biarcs". Engineering with Computers 18, n.º 1 (29 de abril de 2002): 59–65. http://dx.doi.org/10.1007/s003660200005.
Texto completoTesis sobre el tema "Data approximation"
Ross, Colin. "Applications of data fusion in data approximation". Thesis, University of Huddersfield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.247372.
Texto completoDeligiannakis, Antonios. "Accurate data approximation in constrained environments". College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2681.
Texto completoThesis research directed by: Computer Science. Title from abstract of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Tomek, Peter. "Approximation of Terrain Data Utilizing Splines". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236488.
Texto completoCao, Phuong Thao. "Approximation of OLAP queries on data warehouses". Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00905292.
Texto completoLehman, Eric (Eric Allen) 1970. "Approximation algorithms for grammar-based data compression". Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87172.
Texto completoIncludes bibliographical references (p. 109-113).
This thesis considers the smallest grammar problem: find the smallest context-free grammar that generates exactly one given string. We show that this problem is intractable, and so our objective is to find approximation algorithms. This simple question is connected to many areas of research. Most importantly, there is a link to data compression; instead of storing a long string, one can store a small grammar that generates it. A small grammar for a string also naturally brings out underlying patterns, a fact that is useful, for example, in DNA analysis. Moreover, the size of the smallest context-free grammar generating a string can be regarded as a computable relaxation of Kolmogorov complexity. Finally, work on the smallest grammar problem qualitatively extends the study of approximation algorithms to hierarchically-structured objects. In this thesis, we establish hardness results, evaluate several previously proposed algorithms, and then present new procedures with much stronger approximation guarantees.
by Eric Lehman.
Ph.D.
Hou, Jun. "Function Approximation and Classification with Perturbed Data". The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618266875924225.
Texto completoZaman, Muhammad Adib Uz. "Bicubic L1 Spline Fits for 3D Data Approximation". Thesis, Northern Illinois University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10751900.
Texto completoUnivariate cubic L1 spline fits have been successful to preserve the shapes of 2D data with abrupt changes. The reason is that the minimization of L1 norm of the data is considered, as opposite to L2 norm. While univariate L1 spline fits for 2D data are discussed by many, bivariate L1 spline fits for 3D data are yet to be fully explored. This thesis aims to develop bicubic L1 spline fits for 3D data approximation. This can be achieved by solving a bi-level optimization problem. One level is bivariate cubic spline interpolation and the other level is L1 error minimization. In the first level, a bicubic interpolated spline surface will be constructed on a rectangular grid with necessary first and second order derivative values estimated by using a 5-point window algorithm for univariate L 1 interpolation. In the second level, the absolute error (i.e. L1 norm) will be minimized using an iterative gradient search. This study may be extended to higher dimensional cubic L 1 spline fits research.
Cooper, Philip. "Rational approximation of discrete data with asymptotic behaviour". Thesis, University of Huddersfield, 2007. http://eprints.hud.ac.uk/id/eprint/2026/.
Texto completoSchmid, Dominik. "Scattered data approximation on the rotation group and generalizations". Aachen Shaker, 2009. http://d-nb.info/995021562/04.
Texto completoMcQuarrie, Shane Alexander. "Data Assimilation in the Boussinesq Approximation for Mantle Convection". BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/6951.
Texto completoLibros sobre el tema "Data approximation"
Iske, Armin. Approximation Theory and Algorithms for Data Analysis. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05228-7.
Texto completoMotwani, Rajeev. Lecture notes on approximation algorithms. Stanford, CA: Dept. of Computer Science, Stanford University, 1992.
Buscar texto completoC, Mason J. y Cox M. G, eds. Algorithms for approximation II: Based on the proceedings of the Second International Conference on Algorithms for Approximation, held at Royal Military College of Science, Shrivenham, July 1988. London: Chapman and Hall, 1990.
Buscar texto completoFranke, Richard. Recent advances in the approximation of surfaces from scattered data. Monterey, Calif: Naval Postgraduate School, 1987.
Buscar texto completoIvanov, Viktor Vladimirovich. Metody vychisleniĭ na ĖVM: Spravochnoe posobie. Kiev: Nauk. dumka, 1986.
Buscar texto completoFranke, Richard H. Least squares surface approximation to scattered data using multiquadric functions. Monterey, Calif: Naval Postgraduate School, 1993.
Buscar texto completoMolchanov, I. N. Mashinnye metody reshenii͡a︡ prikladnykh zadach algebra, priblizhenie funkt͡s︡iĭ. Kiev: Nauk. dumka, 1987.
Buscar texto completoK, Ray Bimal, ed. Polygonal approximation and scale-space analysis. Oakville, Ont: Apple Academic Press, 2013.
Buscar texto completoC, Mason J., Cox M. G y Institute of Mathematics and Its Applications., eds. Algorithms for approximation: Based on the proceedings of the IMA Conference on Algorithms for the Approximation of Functions and Data, held at the Royal Military College of Science, Shrivenham, July 1985. Oxford [Oxfordshire]: Clarendon Press, 1987.
Buscar texto completoEitan, Tadmor, Institute for Computer Applications in Science and Engineering. y Langley Research Center, eds. Recovering pointwise values of discontinuous data within spectral accuracy. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1985.
Buscar texto completoCapítulos de libros sobre el tema "Data approximation"
Shekhar, Shashi y Hui Xiong. "Data Approximation". En Encyclopedia of GIS, 203. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_237.
Texto completoHutchings, Matthew y Bertrand Gauthier. "Local Optimisation of Nyström Samples Through Stochastic Gradient Descent". En Machine Learning, Optimization, and Data Science, 123–40. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25599-1_10.
Texto completoMarkovsky, Ivan. "From Data to Models". En Low-Rank Approximation, 37–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89620-5_2.
Texto completoDeng, Shaobo, Huihui Lu, Sujie Guan, Min Li y Hui Wang. "Approximation Relation for Rough Sets". En Data Mining and Big Data, 402–17. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7502-7_38.
Texto completoRengaswamy, Raghunathan y Resmi Suresh. "Function Approximation Methods". En Data Science for Engineers, 175–252. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/b23276-6.
Texto completoIske, Armin. "Euclidean Approximation". En Approximation Theory and Algorithms for Data Analysis, 103–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05228-7_4.
Texto completoIske, Armin. "Chebyshev Approximation". En Approximation Theory and Algorithms for Data Analysis, 139–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05228-7_5.
Texto completoMarkovsky, Ivan. "Data-Driven Filtering and Control". En Low-Rank Approximation, 161–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89620-5_6.
Texto completoAdir, Allon, Ehud Aharoni, Nir Drucker, Ronen Levy, Hayim Shaul y Omri Soceanu. "Approximation Methods Part II: Approximations of Standard Functions". En Homomorphic Encryption for Data Science (HE4DS), 125–47. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65494-7_6.
Texto completoWu, Weili, Yi Li, Panos M. Pardalos y Ding-Zhu Du. "Data-Dependent Approximation in Social Computing". En Approximation and Optimization, 27–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12767-1_3.
Texto completoActas de conferencias sobre el tema "Data approximation"
Ma, Guanqun, David Lenz, Tom Peterka, Hanqi Guo y Bei Wang. "Critical Point Extraction from Multivariate Functional Approximation". En 2024 IEEE Topological Data Analysis and Visualization (TopoInVis), 12–22. IEEE, 2024. http://dx.doi.org/10.1109/topoinvis64104.2024.00006.
Texto completoSahrom, Nor Ashikin, Mohammad Izat Emir Zulkifly y Siti Nur Idara Rosli. "Interval-Valued Fuzzy Bézier Surface Approximation". En 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/aidas63860.2024.10730727.
Texto completoBarbas, Petros, Aristidis G. Vrahatis y Sotiris K. Tasoulis. "RLAC: Random Line Approximation Clustering". En 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671596.
Texto completoZhao, Danfeng, Zhou Huang, Feng Zhou, Antonio Liotta y Dongmei Huang. "An Approximation Method for Large Graph Similarity". En 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378447.
Texto completoDas, Abhinandan, Johannes Gehrke y Mirek Riedewald. "Approximation techniques for spatial data". En the 2004 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1007568.1007646.
Texto completoFreedman, Daniel y Pavel Kisilev. "Fast Data Reduction via KDE Approximation". En 2009 Data Compression Conference (DCC). IEEE, 2009. http://dx.doi.org/10.1109/dcc.2009.47.
Texto completoPanda, Biswanath, Mirek Riedewald, Johannes Gehrke y Stephen B. Pope. "High-Speed Function Approximation". En Seventh IEEE International Conference on Data Mining (ICDM 2007). IEEE, 2007. http://dx.doi.org/10.1109/icdm.2007.107.
Texto completoHuang, Zhou y Feng Zhou. "An Approximation Method for Querying Similar Large Graphs". En 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020310.
Texto completoShahcheraghi, Maryam, Trevor Cappon, Samet Oymak, Evangelos Papalexakis, Eamonn Keogh, Zachary Zimmerman y Philip Brisk. "Matrix Profile Index Approximation for Streaming Time Series". En 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671484.
Texto completoKannan, Ramakrishnan, Mariya Ishteva y Haesun Park. "Bounded Matrix Low Rank Approximation". En 2012 IEEE 12th International Conference on Data Mining (ICDM). IEEE, 2012. http://dx.doi.org/10.1109/icdm.2012.131.
Texto completoInformes sobre el tema "Data approximation"
Franke, Richard, Hans Hagen y Gregory M. Nielson. Least Squares Surface Approximation to Scattered Data Using Multiquadric Functions. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 1992. http://dx.doi.org/10.21236/ada259804.
Texto completoRay, Jaideep, Matthew Barone, Stefan Domino, Tania Banerjee y Sanjay Ranka. Verification of Data-Driven Models of Physical Phenomena using Interpretable Approximation. Office of Scientific and Technical Information (OSTI), septiembre de 2021. http://dx.doi.org/10.2172/1821318.
Texto completoBaraniuk, Richard, Ronald DeVore, Sanjeev Kulkarni, Andrew Kurdila, Stanley Osher, Guergana Petrova, Robert Sharpley, Richard Tsai y Hongkai Zhao. Model Classes, Approximation, and Metrics for Dynamic Processing of Urban Terrain Data. Fort Belvoir, VA: Defense Technical Information Center, enero de 2013. http://dx.doi.org/10.21236/ada586168.
Texto completoFranke, Richard. Using Legendre Functions for Spatial Covariance Approximation and Investigation of Radial Nonisotrophy for NOGAPS Data. Fort Belvoir, VA: Defense Technical Information Center, enero de 2001. http://dx.doi.org/10.21236/ada389396.
Texto completoWu, Yan, Sonia Fahmy y Ness B. Shroff. On the Construction of a Maximum-Lifetime Data Gathering Tree in Sensor Networks: NP-Completeness and Approximation Algorithm. Fort Belvoir, VA: Defense Technical Information Center, enero de 2008. http://dx.doi.org/10.21236/ada517885.
Texto completoShah, Rajiv R. High-Level Adaptive Signal Processing Architecture with Applications to Radar Non-Gaussian Clutter. Volume 2. A New Technique for Distribution Approximation of Random Data. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1995. http://dx.doi.org/10.21236/ada300902.
Texto completoGorton, O. y J. Escher. Cross Sections for Neutron-Induced Reactions from Surrogate Data: Assessing the Use of the Weisskopf-Ewing Approximation for (n,n') and (n,2n) Reactions. Office of Scientific and Technical Information (OSTI), septiembre de 2020. http://dx.doi.org/10.2172/1668500.
Texto completoGuan, Jiajing, Sophia Bragdon y Jay Clausen. Predicting soil moisture content using Physics-Informed Neural Networks (PINNs). Engineer Research and Development Center (U.S.), agosto de 2024. http://dx.doi.org/10.21079/11681/48794.
Texto completoBunn, M. I., T. R. Carter, H. A. J. Russell y C. E. Logan. A semiquantitative representation of uncertainty for the 3D Paleozoic bedrock model of Southern Ontario. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331658.
Texto completoRofman, Rafael, Joaquín Baliña y Emanuel López. Evaluating the Impact of COVID-19 on Pension Systems in Latin America and the Caribbean. The Case of Argentina. Inter-American Development Bank, octubre de 2022. http://dx.doi.org/10.18235/0004508.
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