Relevant bibliographies by topics / Pattern recognition

Academic literature on the topic 'Pattern recognition'

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Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Pattern recognition"

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Mikhailov, Alexei. "Indexing-Based Pattern Recognition." Advanced Materials Research 403-408 (November 2011): 5254–59. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5254.

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The paper discusses the mathematics of pattern indexing and its applications to recognition of visual patterns and classification of objects that are represented by objects-properties matrices. It is shown that (a) pattern indexes can be represented by collections of inverted patterns, (b) solutions to pattern classification problems can be found as intersections of inverted patterns and, thus, matching of original patterns avoided.
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Klein, Freada Kapor, and Ana Díaz-Hernández. "Pattern recognition." XRDS: Crossroads, The ACM Magazine for Students 20, no. 4 (June 2014): 20–23. http://dx.doi.org/10.1145/2604991.

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Rutkove, S. B. "Pattern recognition." Neurology 61, no. 4 (August 25, 2003): 585–86. http://dx.doi.org/10.1212/01.wnl.0000078930.98769.11.

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Heidelbaugh, Joel J. "Pattern Recognition." Primary Care: Clinics in Office Practice 42, no. 4 (December 2015): xiii—xiv. http://dx.doi.org/10.1016/j.pop.2015.09.002.

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Lavine, Barry K. "Pattern Recognition." Critical Reviews in Analytical Chemistry 36, no. 3-4 (December 2006): 153–61. http://dx.doi.org/10.1080/10408340600969411.

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Dixon, Jennifer. "Pattern recognition." Lancet 360, no. 9343 (November 2002): 1418. http://dx.doi.org/10.1016/s0140-6736(02)11396-1.

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Holmström, Lasse, and Petri Koistinen. "Pattern recognition." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 4 (June 3, 2010): 404–13. http://dx.doi.org/10.1002/wics.99.

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Narayan, Ritushree, and Puja Mishra. "Pattern Recognition of Jharkhand Tribal Language." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 267–71. http://dx.doi.org/10.31142/ijtsrd10854.

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Dutta Majumder, D. "Fuzzy sets in pattern recognition, image analysis and automatic speech recognition." Applications of Mathematics 30, no. 4 (1985): 237–54. http://dx.doi.org/10.21136/am.1985.104148.

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Cheeney, R. F. "Pattern recognition and pattern analysis." Geological Magazine 122, no. 2 (March 1985): 203–4. http://dx.doi.org/10.1017/s0016756800031150.

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Dissertations / Theses on the topic "Pattern recognition"

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An, Kyung Hee. "Concurrent Pattern Recognition and Optical Character Recognition." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332598/.

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The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.
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Yao, Xiaoqiang. "Pattern-recognition scheduling." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1177698616.

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Petheram, R. J. "Automatic pattern recognition." Thesis, University of Nottingham, 1989. http://eprints.nottingham.ac.uk/28974/.

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In this thesis the author presents a new method for the location, extraction and normalisation of discrete objects found in digital images. The extraction is by means of sub-pixcel contour following around the object. The normalisation obtains and removes the information concerning size, orientation and location of the object within an image. Analyses of the results are carried out to determine the confidence in recognition of patterns, and methods of cross correlation of object descriptions using Fourier transforms are demonstrated.
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Choakjarernwanit, Naruetep. "Statistical pattern recognition." Thesis, University of Surrey, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306586.

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Placide, Eustache. "Hybrid pattern recognition." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 1987. http://digitalcommons.auctr.edu/dissertations/3018.

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There are two basic approaches to pattern recognition: decision-theoretic and syntactic. However, in actual applications, a combination of both may be needed. One such hybrid technique consists of syntactic method coupled with stochasticity in its grammar. Randomness in the syntactic case is caused due to noise and insufficient information about characteristics of pattern classes. To absorb the effect of this randomness, the grammar must be generalized to include the probabilities of production rules. In this paper, a preliminary discussion of issues involved with hybrid techniques, in general, and stochastic grammars, in particular, is provided. An efficient algorithm for an automatic learning of production probabilities is devised. Concepts are illustrated via examples.
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Smeraldi, Fabrizio. "Attention-driven pattern recognition /." [S.l.] : [s.n.], 2000. http://library.epfl.ch/theses/?nr=2153.

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Angstenberger, Larisa. "Dynamic fuzzy pattern recognition." [S.l.] : [s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962701106.

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Weir, D. K. "Pattern recognition of electrocardiograms." Thesis, University of Ulster, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355922.

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Lindén, Fredrik. "Fractal pattern recognition and recreation." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-181224.

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It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves as fractals, and by studying the fractal dimension and lacunarity we can make a prediction of where the salt can be located. I have also investigated three different recreation techniques, so that one can go from parameters values (fractal dimension and lacunarity) back to a possible recreation. It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves as fractals, and by studying the fractal dimension and lacunarity we can make a prediction of where the salt can be located. I have also investigated three different recreation techniques, so that one can go from parameters values (fractal dimension and lacunarity) back to a possible recreation.
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Sun, Te-Wei. "DEPARS, design pattern recognition system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq28464.pdf.

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Books on the topic "Pattern recognition"

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William, Gibson. Pattern recognition. New York: G.P. Putnam's Sons, 2003.

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1967-, Koutroumbas Konstantinos, and ScienceDirect (Online service), eds. Pattern recognition. 4th ed. Amsterdam: Elsevier/Academic Press, 2009.

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Akata, Zeynep, Andreas Geiger, and Torsten Sattler, eds. Pattern Recognition. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71278-5.

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Roman-Rangel, Edgar, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, and José Arturo Olvera-López, eds. Pattern Recognition. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77004-4.

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Bauckhage, Christian, Juergen Gall, and Alexander Schwing, eds. Pattern Recognition. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92659-5.

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Wallraven, Christian, Qingshan Liu, and Hajime Nagahara, eds. Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02444-3.

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Wallraven, Christian, Qingshan Liu, and Hajime Nagahara, eds. Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02375-0.

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Vergara-Villegas, Osslan Osiris, Vianey Guadalupe Cruz-Sánchez, Juan Humberto Sossa-Azuela, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, and José Arturo Olvera-López, eds. Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07750-0.

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Andres, Björn, Florian Bernard, Daniel Cremers, Simone Frintrop, Bastian Goldlücke, and Ivo Ihrke, eds. Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16788-1.

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Theodoridis, S. Pattern recognition. 2nd ed. Amsterdam: Academic Press, 2003.

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Book chapters on the topic "Pattern recognition"

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Marques de Sá, Joaquim P. "Pattern Discrimination." In Pattern Recognition, 21–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_2.

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Marques de Sá, Joaquim P. "Basic Notions." In Pattern Recognition, 1–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_1.

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Marques de Sá, Joaquim P. "Data Clustering." In Pattern Recognition, 53–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_3.

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Marques de Sá, Joaquim P. "Statistical Classification." In Pattern Recognition, 79–145. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_4.

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Marques de Sá, Joaquim P. "Neural Networks." In Pattern Recognition, 147–242. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_5.

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Marques de Sá, Joaquim P. "Structural Pattern Recognition." In Pattern Recognition, 243–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_6.

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"PATTERN RECOGNITION." In Pattern Recognition, 1–52. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch1.

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"PATTERN RECOGNITION." In Pattern Recognition, 53–99. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch2.

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"CLASSIFICATION WITH REJECTION PROBLEM FORMULATION AND AN OVERVIEW." In Pattern Recognition, 101–31. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch3.

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"EVALUATING PATTERN RECOGNITION PROBLEM." In Pattern Recognition, 133–57. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch4.

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Conference papers on the topic "Pattern recognition"

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Lu, Jianyi, and T. William Lin. "Adaptive pattern recognition for binary images." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.thx4.

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It is understood that the correlation process between similar patterns generally introduces ambiguity in decision making. This problem is much more profound in the binary pattern recognition process. For example, the cross-correlation between letters E and F exhibits the same main peak intensity as that in the autocorrelation of letter F. Many attempts have been made to overcome this difficulty, including the recognition technique based on a comparison of pattern’s perimeters, and the recognition of patterns in moment spaces or feature spaces. However, the phenomenon that causes the ambiguity can be converted and utilized as a feedback parameter when an adaptive process scheme is chosen in pattern recognition. In this paper, the relationship between pattern shapes and correlation results is analyzed first, followed by a proposed hybrid optical-electronic adaptive joint transform correlator. The adaptive capability of the system is achieved through interfacing between an optical correlator and a computer. The intensity distribution of correlation peaks detected in the optical correlator serves as a feedback to update reference images in the input plane of the correlator, so that an optimal decision can be made for the recognition process through adaptive iterations. In the iteration process, the saturating phenomenon in the intensity of correlation peaks is used as a guideline in designing a proper feedback scheme. Several uses in binary pattern recognition are demonstrated with results obtained from computer simulations as well as experimentally.
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Mikhailov, Alexei M., and Mikhail F. Karavay. "Pattern recognition by pattern inversion." In The Second International Conference on Image, Video Processing and Artificial Intelligence, edited by Ruidan Su. SPIE, 2019. http://dx.doi.org/10.1117/12.2539069.

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Kim, B. F., and J. Bohandy. "Partial Matching in a Hierarchical Pattern Recognition System." In Machine Vision. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/mv.1987.thb3.

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A pattern recognition system has recently been described,(1) in which images are represented by a hierarchical sequence of patterns of decreasing size, forming a type of pyramid structure (similar in some respects to resolution pyramids). Pattern recognition occurs by comparison of sub-patterns of images in each level with array patterns stored in a memory associated with each level. In these comparisons, a matching criterion determines, at each level, the degree to which the patterns being compared must match in order to be considered equivalent. The purpose of this report is to present some results on how the matching criteria affect the performance of this type of system.
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Morris, G. Michael. "Photon-Limited Pattern Recognition." In Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/qlip.1989.mc1.

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Often input scenes contain a vast amount of information, which tends to make pattern-recognition decisions laborious and time consuming. In traditional digital pattern recognition methods, one digitizes the input scene using a two-dimensional detector, e.g. a solid-state photodiode array and a frame store. If the detector consists of, say, a 1000 x 1000 array of detection elements, then one has to process a million points of data. This is too much information for even very large computers to process in real time, so one generally transforms the input information into some sort of feature-space representation, e.g. through the use of edge-enhanced images, and makes the recognition decision based on this reduced data set.
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Magnus, A. L., and S. C. Gustafson. "Inquisitive pattern recognition." In Proceedings of the Third International Conference on Information Fusion. IEEE, 2000. http://dx.doi.org/10.1109/ific.2000.862459.

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Pinkse, P. W. H., S. A. Goorden, M. Horstmann, B. Skoric, and A. P. Mosk. "Quantum pattern recognition." In 2013 Conference on Lasers & Electro-Optics Europe & International Quantum Electronics Conference CLEO EUROPE/IQEC. IEEE, 2013. http://dx.doi.org/10.1109/cleoe-iqec.2013.6801621.

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Krajewski, Jarek, Thomas Schnupp, Sebastian Schnieder, David Sommer, Christian Heinze, and Martin Golz. "Pattern recognition methods." In the 7th International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1931344.1931369.

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Przybylek, Michal R. "Algebraic pattern recognition." In Fifth International Conference on Graphic and Image Processing, edited by Yulin Wang, Xudong Jiang, Ming Yang, David Zhang, and Xie Yi. SPIE, 2014. http://dx.doi.org/10.1117/12.2050883.

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Casasent, David. "Optical pattern recognition." In Critical Review Collection. SPIE, 1992. http://dx.doi.org/10.1117/12.161585.

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Alfalou, A., C. Brosseau, and M. S. Alam. "Smart pattern recognition." In SPIE Defense, Security, and Sensing, edited by David Casasent and Tien-Hsin Chao. SPIE, 2013. http://dx.doi.org/10.1117/12.2018249.

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Reports on the topic "Pattern recognition"

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Montana, Shaun P. Statistical Pattern Recognition Tool Upgrades. Fort Belvoir, VA: Defense Technical Information Center, July 1999. http://dx.doi.org/10.21236/ada367916.

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Montana, Shaun P. A Statistical Pattern Recognition Tool. Fort Belvoir, VA: Defense Technical Information Center, June 1997. http://dx.doi.org/10.21236/ada327388.

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Paek, Eung Gi. Optical pattern recognition with microlasers. Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6017.

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McKenney, B., M. McGrain, A. Klinger, J. Aggarwal, N. George, and R. Haralick. Soviet image pattern recognition research. Office of Scientific and Technical Information (OSTI), December 1989. http://dx.doi.org/10.2172/6764408.

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Pelletier, Justin M. Pattern Recognition Software: Functional Methodology Document. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada494881.

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George, Nicholas. Optoelectronic Workshops II. Automatic Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada203779.

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MacDonald, Jacqueline A., and Mitchell J. Small. Statistical Methods for UXO Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, December 2007. http://dx.doi.org/10.21236/ada603920.

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Olsen, Jamieson, Tiehui Ted Liu, Jim Hoff, Zhen Hu, Jim Yuan Wu, and Zijun Xu. An FPGA-based Pattern Recognition Associative Memory. Office of Scientific and Technical Information (OSTI), July 2018. http://dx.doi.org/10.2172/1480099.

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Rizki, Mateen M., and Louis A. Tamburino. HELPR: Hybrid Evolutionary Learning for Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, December 2005. http://dx.doi.org/10.21236/ada446893.

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León, Carlos, Paolo Barucca, Oscar Acero, Gerardo Gage, and Fabio Ortega. Pattern recognition of financial institutions’ payment behavior. Banco de la República de Colombia, September 2020. http://dx.doi.org/10.32468/be.1130.

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