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Bibliografías temáticas / Pattern recognition / Artículos de revistas

Artículos de revistas sobre el tema "Pattern recognition"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 7 de julio de 2024

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1

Mikhailov, Alexei. "Indexing-Based Pattern Recognition". Advanced Materials Research 403-408 (noviembre de 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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2

Klein, Freada Kapor y Ana Díaz-Hernández. "Pattern recognition". XRDS: Crossroads, The ACM Magazine for Students 20, n.º 4 (junio de 2014): 20–23. http://dx.doi.org/10.1145/2604991.

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3

Rutkove, S. B. "Pattern recognition". Neurology 61, n.º 4 (25 de agosto de 2003): 585–86. http://dx.doi.org/10.1212/01.wnl.0000078930.98769.11.

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4

Heidelbaugh, Joel J. "Pattern Recognition". Primary Care: Clinics in Office Practice 42, n.º 4 (diciembre de 2015): xiii—xiv. http://dx.doi.org/10.1016/j.pop.2015.09.002.

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5

Lavine, Barry K. "Pattern Recognition". Critical Reviews in Analytical Chemistry 36, n.º 3-4 (diciembre de 2006): 153–61. http://dx.doi.org/10.1080/10408340600969411.

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6

Dixon, Jennifer. "Pattern recognition". Lancet 360, n.º 9343 (noviembre de 2002): 1418. http://dx.doi.org/10.1016/s0140-6736(02)11396-1.

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7

Holmström, Lasse y Petri Koistinen. "Pattern recognition". Wiley Interdisciplinary Reviews: Computational Statistics 2, n.º 4 (3 de junio de 2010): 404–13. http://dx.doi.org/10.1002/wics.99.

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8

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

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9

Dutta Majumder, D. "Fuzzy sets in pattern recognition, image analysis and automatic speech recognition". Applications of Mathematics 30, n.º 4 (1985): 237–54. http://dx.doi.org/10.21136/am.1985.104148.

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10

Cheeney, R. F. "Pattern recognition and pattern analysis". Geological Magazine 122, n.º 2 (marzo de 1985): 203–4. http://dx.doi.org/10.1017/s0016756800031150.

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11

Koyama, Motoaki. "Pattern recognition apparatus with retrieval reference patterns". Journal of the Acoustical Society of America 95, n.º 1 (enero de 1994): 592. http://dx.doi.org/10.1121/1.408283.

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12

Hill, Andrea A. y Gretchen E. Diehl. "Identifying the Patterns of Pattern Recognition Receptors". Immunity 49, n.º 3 (septiembre de 2018): 389–91. http://dx.doi.org/10.1016/j.immuni.2018.08.027.

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13

Huang, K. Y., K. S. Fu, S. W. Cheng y Z. S. Lin. "Syntactic pattern recognition and Hough transformation for reconstruction of seismic patterns". GEOPHYSICS 52, n.º 12 (diciembre de 1987): 1612–20. http://dx.doi.org/10.1190/1.1442278.

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Hierarchical syntactic pattern recognition and the Hough transformation are proposed for automatic recognition and reconstruction of seismic patterns in seismograms. In the first step, the patterns are hierarchically decomposed or recognized into single patterns, straight‐line patterns, or hyperbolic patterns, using syntactic pattern recognition. In the second step, the Hough transformation technique is used for reconstruction, pattern by pattern. The system of syntactic seismic pattern recognition includes envelope generation, a linking process in the seismogram, segmentation, primitive recognition, grammatical inference, and syntax analysis. The seismic patterns are automatically recognized and reconstructed.

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14

Solus, Dávid, Ľuboš Ovseník y Ján Turán. "Microchip Pattern Recognition Based on Optical Correlator". Acta Electrotechnica et Informatica 17, n.º 2 (1 de junio de 2017): 38–42. http://dx.doi.org/10.15546/aeei-2017-0014.

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15

Kim, E. E. "Pattern Recognition Neuroradiology". Journal of Nuclear Medicine 54, n.º 3 (18 de diciembre de 2012): 491. http://dx.doi.org/10.2967/jnumed.112.116202.

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16

HAYASHI, Takaya, Tohru NAKAMURA y Akinori TAKAOKA. "Pattern recognition receptors". Japanese Journal of Clinical Immunology 34, n.º 5 (2011): 329–45. http://dx.doi.org/10.2177/jsci.34.329.

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17

Deepa, Dr S. T. y Praneetha V. "Iris Pattern Recognition". IOSR Journal of Computer Engineering 18, n.º 04 (abril de 2016): 43–50. http://dx.doi.org/10.9790/0661-1804024350.

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18

Stentiford, Frederick W. M. "Pattern recognition system". Journal of the Acoustical Society of America 91, n.º 1 (enero de 1992): 546. http://dx.doi.org/10.1121/1.402660.

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19

Davis, J. Wade. "Statistical Pattern Recognition". Technometrics 45, n.º 4 (noviembre de 2003): 368. http://dx.doi.org/10.1198/tech.2003.s172.

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20

Craig, James C. y Paul M. Evans. "Vibrotactile pattern recognition". Journal of the Acoustical Society of America 79, S1 (mayo de 1986): S73. http://dx.doi.org/10.1121/1.2023367.

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21

Chin, G. J. "BIOCHEMISTRY: Pattern Recognition". Science 310, n.º 5751 (18 de noviembre de 2005): 1091e—1093e. http://dx.doi.org/10.1126/science.310.5751.1091e.

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22

Griffin, William A. "Affect Pattern Recognition". Marriage & Family Review 34, n.º 1-2 (enero de 2002): 139–63. http://dx.doi.org/10.1300/j002v34n01_07.

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23

Takahashi, Satoshi y Shigeki Sagayama. "Pattern recognition method". Journal of the Acoustical Society of America 105, n.º 3 (1999): 1450. http://dx.doi.org/10.1121/1.426677.

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24

MacFie, H. J. H. "Chemical Pattern Recognition". Analytica Chimica Acta 193 (1987): 410–11. http://dx.doi.org/10.1016/s0003-2670(00)86201-7.

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25

Gordon, Siamon. "Pattern Recognition Receptors". Cell 111, n.º 7 (diciembre de 2002): 927–30. http://dx.doi.org/10.1016/s0092-8674(02)01201-1.

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26

Lavine, B. K. "Multivariate pattern recognition". TrAC Trends in Analytical Chemistry 13, n.º 8 (septiembre de 1994): 340–41. http://dx.doi.org/10.1016/0165-9936(94)87009-8.

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27

Inkinen, Sami J. "Pattern-Recognition Transforms". Signal Processing 35, n.º 1 (enero de 1994): 100–102. http://dx.doi.org/10.1016/0165-1684(94)90201-1.

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28

Wong, Kum Thong, Chong Tin Tan y Tchoyoson Lim. "Beyond Pattern Recognition". Neuroimaging Clinics of North America 33, n.º 1 (febrero de 2023): 225–33. http://dx.doi.org/10.1016/j.nic.2022.07.018.

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29

Sipe, Michael A. "Statistical Pattern Recognition". Pattern Analysis & Applications 4, n.º 1 (marzo de 2001): 71–72. http://dx.doi.org/10.1007/s100440170028.

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30

Mahadevia, Soham. "Pattern Recognition Neuroradiology". Academic Radiology 19, n.º 10 (octubre de 2012): 1305–6. http://dx.doi.org/10.1016/j.acra.2012.03.030.

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31

Tate, A. R. y S. Howells. "Pattern recognition analysis". NMR in Biomedicine 11, n.º 4-5 (junio de 1998): 147. http://dx.doi.org/10.1002/(sici)1099-1492(199806/08)11:4/5<147::aid-nbm533>3.0.co;2-#.

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32

Rorvig, Mark E. "Pattern Recognition Engineering". Information Processing & Management 31, n.º 2 (marzo de 1995): 257–58. http://dx.doi.org/10.1016/0306-4573(95)80046-v.

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33

Koh, Young-Sang. "Pattern-recognition receptors and recognition of pathogens". Journal of Medicine and Life Science 6, n.º 3 (1 de junio de 2009): 148–51. http://dx.doi.org/10.22730/jmls.2009.6.3.148.

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The mammalian host has evolved innate immune strategies to detect and respond to rapidly to diverse group of microorganisms. Microbial recognition is achieved by pattern-recognition receptors (PRRs) such as toll-like receptors (TLRs) and Nod-like receptor (NLRs). PRRs detect pathogen-associated molecular patterns (PAMPs) and activate downstream signalling pathways. NLRs have been proposed thal these cytosolic proteins regulate the cysteine protease caspase-1 within a multiprotein complex known as the inflammasome. Activation of caspase-1 leads to the cleavage and activation of proinflammatory cytokines such as interleukin-1β (lL-1β) and IL-18.

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34

Kumar, B. V. K. V., M. Savvides y Chunyan Xie. "Correlation Pattern Recognition for Face Recognition". Proceedings of the IEEE 94, n.º 11 (noviembre de 2006): 1963–76. http://dx.doi.org/10.1109/jproc.2006.884094.

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35

Brauer, Donna J. "A Method for Pattern Recognition". Research and Theory for Nursing Practice 20, n.º 4 (diciembre de 2006): 277–90. http://dx.doi.org/10.1891/rtnp-v20i4a004.

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Although pattern is a dominant concept in nursing science, only Newman’s method for recognizing pattern has been fully articulated and widely used in research about the human health experience. This article proposes an alternative, less costly method to facilitate research with larger numbers of participants in clinical settings. Cluster analysis, a quasi-quantitative technique, and content analysis were combined to produce a technique for recognizing patterns of person–environment interaction. Results from two studies with persons experiencing a highly variable chronic illness, rheumatoid arthritis, indicated that this new approach identifies distinct common patterns of person–environment interaction. Sufficient detail about the nature of each pattern resulted to facilitate further knowledge development about the health experience and to provide guidance in structuring nursing care.

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36

Tan, Z., B. S. Hepburn, C. Tucker y M. K. Ali. "Pattern recognition using chaotic neural networks". Discrete Dynamics in Nature and Society 2, n.º 4 (1998): 243–47. http://dx.doi.org/10.1155/s1026022698000211.

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Pattern recognition by chaotic neural networks is studied using a hyperchaotic neural network as model. Virtual basins of attraction are introduced around unstable periodic orbits which are then used as patterns. Search for periodic orbits in dynamical systems is treated as a process of pattern recognition. The role of synapses on patterns in chaotic networks is discussed. It is shown that distorted states having only limited information of the patterns are successfully recognized.

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37

Singh, Chetanpal. "Machine Learning in Pattern Recognition". European Journal of Engineering and Technology Research 8, n.º 2 (11 de abril de 2023): 63–68. http://dx.doi.org/10.24018/ejeng.2023.8.2.3025.

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Supervised or unsupervised classification is the main objective of pattern recognition. The statistical approach is the most popular approach that is practised among the several frameworks where pattern recognition is initially formulated. In the recent past, the neural network technique and the methodology scheme from the statistical learning theory have garnered the attention of people. It requires proper attention to deal with the design of the recognition system. There are several issues associated with the design of the recognition system. They are the pattern class definition, sensing environment and representation extraction and selection of features, cluster analysis, classifier design, learning, and choosing the training and test samples. There is no solution to the general issue of recognizing complex patterns associated with arbitrary patterns. Data mining, web searching, and retrieval of multimedia are the various emerging applications that require proper and effective regulation techniques. The main purpose of this paper is to give a detailed overview of the various methods that can be used in the different stages of the pattern recognition system. The paper also aims to figure out the research topics in the application that can be highlighted in this challenging field.

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38

Kaur, Manpreet. "A Review on Pattern Recognition Using Genetic Algorithms". International journal of Emerging Trends in Science and Technology 04, n.º 05 (2 de junio de 2017): 5213–20. http://dx.doi.org/10.18535/ijetst/v4i5.16.

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39

POTAPOV, ALEXEI B. y M. K. ALI. "PATTERN RECOGNITION WITH HAMILTONIAN DYNAMICS". International Journal of Modern Physics C 12, n.º 05 (junio de 2001): 751–58. http://dx.doi.org/10.1142/s0129183101001948.

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We consider pattern recognition schemes that are based upon Hamiltonian dynamical system. Different oscillatory modes are used for storing and encoding patterns, and the effect of resonance is used for determining the most excited mode. We also propose a new technique for pattern orthogonalization resorting to hidden dimensions. Numerical experiments confirm high storage capacity and absence of false memories for the proposed system. Hamiltonian systems may be important as classical analogs of quantum computing systems or quantum neural networks.

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40

Porter, Tim y Monique Pavel. "Fundamentals of Pattern Recognition". Mathematical Gazette 74, n.º 467 (marzo de 1990): 100. http://dx.doi.org/10.2307/3618903.

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41

Dutt, Vinita, Vikas Chaudhry y Imran Khan. "Pattern Recognition: an Overview". American Journal of Intelligent Systems 2, n.º 1 (31 de agosto de 2012): 23–27. http://dx.doi.org/10.5923/j.ajis.20120201.04.

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42

Kalyadin, N. I. "Constructivization in pattern recognition". Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki, n.º 2 (abril de 2008): 188–93. http://dx.doi.org/10.20537/vm080263.

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43

Yakubovich, V. A. "Machines Learning Pattern Recognition". Vestnik St. Petersburg University, Mathematics 54, n.º 4 (octubre de 2021): 384–94. http://dx.doi.org/10.1134/s106345412104021x.

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44

Hoenig, Melanie P. y Stewart H. Lecker. "Pattern Recognition versus Pathogenesis". Clinical Journal of the American Society of Nephrology 17, n.º 4 (3 de marzo de 2022): 585–87. http://dx.doi.org/10.2215/cjn.16061221.

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45

Jogi, Joycee, Varsha Sharma, Anju Nayak, Vandana Gupta, Poonam Shakya y Smita Bordoloi. "Pattern Recognition Receptors (PRRs)". International Journal of Current Microbiology and Applied Sciences 7, n.º 09 (10 de septiembre de 2018): 3400–3406. http://dx.doi.org/10.20546/ijcmas.2018.709.422.

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46

Tao, Y., Z. Chen y C. L. Griffis. "Chick feather pattern recognition". IEE Proceedings - Vision, Image, and Signal Processing 151, n.º 5 (2004): 337. http://dx.doi.org/10.1049/ip-vis:20040730.

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47

Gebran, M. y F. Paletou. "Pattern recognition in spectra". Journal of Physics: Conference Series 869 (junio de 2017): 012075. http://dx.doi.org/10.1088/1742-6596/869/1/012075.

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48

Mikhailov, A. M. "Pattern recognition by indexing". Automation and Remote Control 73, n.º 4 (abril de 2012): 717–24. http://dx.doi.org/10.1134/s0005117912040108.

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49

Rizki, M. M., M. A. Zmuda y L. A. Tamburino. "Evolving pattern recognition systems". IEEE Transactions on Evolutionary Computation 6, n.º 6 (diciembre de 2002): 594–609. http://dx.doi.org/10.1109/tevc.2002.806167.

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50

Shamir, Joseph. "Adaptive pattern recognition correlators". Optical Engineering 36, n.º 10 (1 de octubre de 1997): 2675. http://dx.doi.org/10.1117/1.601318.

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