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Relevant bibliographies by topics / Self-organizing map

Academic literature on the topic 'Self-organizing map'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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Journal articles on the topic "Self-organizing map"

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Rougier, Nicolas P., and Georgios Is Detorakis. "Randomized Self-Organizing Map." Neural Computation 33, no. 8 (July 26, 2021): 2241–73. http://dx.doi.org/10.1162/neco_a_01406.

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We propose a variation of the self-organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies possess random (but controllable) discontinuities that allow for a more flexible self-organization, especially with high-dimensional data. The proposed algorithm is tested on one-, two- and three-dimensional tasks, as well as on the MNIST handwritten digits data set and validated using spectral analysis and topological data analysis tools. We also demonstrate the ability of the randomized self-organizing map to gracefully reorganize itself in case of neural lesion and/or neurogenesis.

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Tatoian, Robert, and Lutz Hamel. "Self-Organizing Map Convergence." International Journal of Service Science, Management, Engineering, and Technology 9, no. 2 (April 2018): 61–84. http://dx.doi.org/10.4018/ijssmet.2018040103.

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Self-organizing maps are artificial neural networks designed for unsupervised machine learning. Here in this article, the authors introduce a new quality measure called the convergence index. The convergence index is a linear combination of map embedding accuracy and estimated topographic accuracy and since it reports a single statistically meaningful number it is perhaps more intuitive to use than other quality measures. The convergence index in the context of clustering problems was proposed by Ultsch as part of his fundamental clustering problem suite as well as real world datasets. First demonstrated is that the convergence index captures the notion that a SOM has learned the multivariate distribution of a training data set by looking at the convergence of the marginals. The convergence index is then used to study the convergence of SOMs with respect to the different parameters that govern self-organizing map learning. One result is that the constant neighborhood function produces better self-organizing map models than the popular Gaussian neighborhood function.

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Kohonen, T. "The self-organizing map." Proceedings of the IEEE 78, no. 9 (1990): 1464–80. http://dx.doi.org/10.1109/5.58325.

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Vuorimaa, Petri. "Fuzzy self-organizing map." Fuzzy Sets and Systems 66, no. 2 (September 1994): 223–31. http://dx.doi.org/10.1016/0165-0114(94)90312-3.

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Kohonen, Teuvo. "The self-organizing map." Neurocomputing 21, no. 1-3 (November 1998): 1–6. http://dx.doi.org/10.1016/s0925-2312(98)00030-7.

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Pal, Sankar K., Biswarup Dasgupta, and Pabitra Mitra. "Rough Self Organizing Map." Applied Intelligence 21, no. 3 (November 2004): 289–99. http://dx.doi.org/10.1023/b:apin.0000043561.99513.69.

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Saini, Naveen, Sriparna Saha, Sahil Mansoori, and Pushpak Bhattacharyya. "Fusion of self-organizing map and granular self-organizing map for microblog summarization." Soft Computing 24, no. 24 (July 11, 2020): 18699–711. http://dx.doi.org/10.1007/s00500-020-05104-2.

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Sampath, R., and A. Saradha. "Alzheimer’s Disease Image Segmentation with Self-Organizing Map Network." Journal of Software 10, no. 6 (June 2015): 670–80. http://dx.doi.org/10.17706//jsw.10.6.67-680.

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Sampath, R., and A. Saradha. "Alzheimer’s Disease Image Segmentation with Self-Organizing Map Network." Journal of Software 10, no. 6 (2015): 670–78. http://dx.doi.org/10.17706//jsw.10.6.670-680.

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FURUKAWA, Tetsuo. "Self-organizing map of a set of self-organizing maps." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 19, no. 6 (2007): 618–26. http://dx.doi.org/10.3156/jsoft.19.6_618.

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Dissertations / Theses on the topic "Self-organizing map"

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Farshad, Tabrizi Seyed Ramin. "The Probabilistic Supervised Self-Organizing Map, PSSOM." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ31828.pdf.

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Choe, Yoonsuck. "Perceptual grouping in a self-organizing map of spiking neurons." Access restricted to users with UT Austin EID Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3025202.

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Schwardt, Martin. "Lösung ausgewählter Routenplanungsprobleme mit Hilfe der self-organizing map." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975255126.

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Pourkia, Javid. "A SELF-ORGANIZING MAP APPROACH FOR HOSPITAL DATA ANALYSIS." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/theses/1553.

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In this work, we utilize Self Organized Maps (SOM) to cluster and classify hospital related data with large dimensions, provided by Medicare website. These data have published every year and it includes numerous measures for each hospital in the nationwide. It might be possible to unearth some correlations in health-care industry by being able to interpreting this dataset, for example by examining the relations between data of immunizations department to readmission records and hospital expenses. It is not feasible to make any sense from these measures altogether using traditional methods (2D or 3D charts, diagrams or graphs, different tables), because as a result of being human, we cannot comprehend more than 3 dimensions with naked eyes. Since it would be very useful if we could correlate the dimensions to each other to discover new patterns and knowledge, SOMs are a type of Artificial Neural Networks that can be trained using unsupervised learning to illustrate complex and high dimensional data by generating a low dimension representation of the training sample. This way, a powerful and easy-to-interpret visualization will be provided for healthcare officials to rapidly identify the correlation between different attributes of the dataset using clusters illustration

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Žáček, Viktor. "Kohonenova samoorganizační mapa." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219527.

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Work deal about self-organizing maps, especially about Kohonen self-organizing map. About creating of aplication, which realize creating and learning of self-organizing map. And about usage of self-organizing map for self-localization of robot.

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Malondkar, Ameya Mohan. "Extending the Growing Hierarchical Self Organizing Maps for a Large Mixed-Attribute Dataset Using Spark MapReduce." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33385.

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In this thesis work, we propose a Map-Reduce variant of the Growing Hierarchical Self Organizing Map (GHSOM) called MR-GHSOM, which is capable of handling mixed attribute datasets of massive size. The Self Organizing Map (SOM) has proved to be a useful unsupervised data analysis algorithm. It projects a high dimensional data onto a lower dimensional grid of neurons. However, the SOM has some limitations owing to its static structure and the incapability to mirror the hierarchical relations in the data. The GHSOM overcomes these shortcomings of the SOM by providing a dynamic structure that adapts its shape according to the input data. It is capable of growing dynamically in terms of the size of the individual neuron layers to represent data at the desired granularity as well as in depth to model the hierarchical relations in the data. However, the training of the GHSOM requires multiple passes over an input dataset. This makes it difficult to use the GHSOM for massive datasets. In this thesis work, we propose a Map-Reduce variant of the GHSOM called MR-GHSOM, which is capable of processing massive datasets. The MR-GHSOM is implemented using the Apache Spark cluster computing engine and leverages the popular Map-Reduce programming model. This enables us to exploit the usefulness and dynamic capabilities of the GHSOM even for a large dataset. Moreover, the conventional GHSOM algorithm can handle datasets with numeric attributes only. This is owing to the fact that it relies heavily on the Euclidean space dissimilarity measures of the attribute vectors. The MR-GHSOM further extends the GHSOM to handle mixed attribute - numeric and categorical - datasets. It accomplishes this by adopting the distance hierarchy approach of managing mixed attribute datasets. The proposed MR-GHSOM is thus capable of handling massive datasets containing mixed attributes. To demonstrate the effectiveness of the MR-GHSOM in terms of clustering of mixed attribute datasets, we present the results produced by the MR-GHSOM on some popular datasets. We further train our MR-GHSOM on a Census dataset containing mixed attributes and provide an analysis of the results.

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Wang, Dali. "Adaptive Double Self-Organizing Map for Clustering Gene Expression Data." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/WangD2003.pdf.

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Mewes, Daniel, and Ch Jacobi. "Analyzing Arctic surface temperatures with Self Organizing-Maps: Influence of the maps size." Universität Leipzig, 2018. https://ul.qucosa.de/id/qucosa%3A31794.

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We use ERA-Interim reanalysis data of 2 meter temperature to perform a pattern analysis of the Arctic temperatures exploiting an artificial neural network called Self Organizing-Map (SOM). The SOM method is used as a cluster analysis tool where the number of clusters has to be specified by the user. The different sized SOMs are analyzed in terms of how the size changes the representation of specific features. The results confirm that the larger the SOM is chosen the larger will be the root mean square error (RMSE) for the given SOM, which is followed by the fact that a larger number of patterns can reproduce more specific features for the temperature.
Wir benutzten das künstliche neuronale Netzwerk Self Organizing-Map (SOM), um eine Musteranalyse von ERA-Interim Reanalysedaten durchzuführen. Es wurden SOMs mit verschiedener Musteranzahl verglichen. Die Ergebnisse zeigen, dass SOMs mit einer größeren Musteranzahl deutlich spezifischere Muster produzieren im Vergleich zu SOMs mit geringen Musteranzahlen. Dies zeigt sich unter anderem in der Betrachtung der mittleren quadratischen Abweichung (RMSE) der Muster zu den zugeordneten ERA Daten.

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Bui, Michael. "Path finding on a spherical self-organizing map using distance transformations." Thesis, The University of Sydney, 2008. http://hdl.handle.net/2123/9290.

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Spatialization methods create visualizations that allow users to analyze high-dimensional data in an intuitive manner and facilitates the extraction of meaningful information. Just as geographic maps are simpli ed representations of geographic spaces, these visualizations are esssentially maps of abstract data spaces that are created through dimensionality reduction. While we are familiar with geographic maps for path planning/ nding applications, research into using maps of high-dimensional spaces for such purposes has been largely ignored. However, literature has shown that it is possible to use these maps to track temporal and state changes within a high-dimensional space. A popular dimensionality reduction method that produces a mapping for these purposes is the Self-Organizing Map. By using its topology preserving capabilities with a colour-based visualization method known as the U-Matrix, state transitions can be visualized as trajectories on the resulting mapping. Through these trajectories, one can gather information on the transition path between two points in the original high-dimensional state space. This raises the interesting question of whether or not the Self-Organizing Map can be used to discover the transition path between two points in an n-dimensional space. In this thesis, we use a spherically structured Self-Organizing Map called the Geodesic Self-Organizing Map for dimensionality reduction and the creation of a topological mapping that approximates the n-dimensional space. We rst present an intuitive method for a user to navigate the surface of the Geodesic SOM. A new application of the distance transformation algorithm is then proposed to compute the path between two points on the surface of the SOM, which corresponds to two points in the data space. Discussions will then follow on how this application could be improved using some form of surface shape analysis. The new approach presented in this thesis would then be evaluated by analyzing the results of using the Geodesic SOM for manifold embedding and by carrying out data analyses using carbon dioxide emissions data.

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Tervonen, J. (Jaakko). "Exploring behaviour patterns with self-organizing map for personalised mental stress detection." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201904131491.

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Abstract. Stress is an important health problem and the cause for many illnesses and working days lost. It is often measured with different questionnaires that capture only the current stress levels and may come in too late for early prevention. They are also prone to subjective inaccuracies since the feeling of stress, and the physiological response to it, have been found to be individual. Real-time stress detectors, trained on biosignals like heart rate variability, exist but majority of them employ supervised learning which requires collecting a large amount of labelled data from each system user. Commonly, they are tested in situations where the stress response is deliberately induced (e.g. laboratory). Thus they may not generalise to real-life conditions where more general behavioural data could be used. In this study the issues with labelling and individuality are addressed by fitting unsupervised stress detection models at several personalisation levels. The method explored, the Self-Organizing Map, is combined with different clustering algorithms to find personal, semi-personal and general behaviour patterns that are converted to stress predictions. Laboratory biosignal-data are used for method validation. To provide an always-on type stress detection, real-life behavioural data consisting of biosignals and smartphone data are experimented on. The results show that personalisation does improve the predictions. The best classification performance for the laboratory data was found with the fully personalised model (F1-score 0.89 vs. 0.45 with the general model) but for the real-life data there was no big difference between fully personal (F1-score 0.57) and general model as long as the behaviour patterns were mapped to stress individually (F1-score 0.60). While the scores also validate the feasibility of SOM for mental stress detection, further research is needed to determine the most suitable and practical level of personalisation and an unambiguous mapping between behaviour patterns and stress.Tiivistelmä. Stressi on merkittävä terveysongelma ja syynä useisiin sairauksiin sekä työpoissaoloihin. Sitä mitataan usein erilaisilla kyselyillä, jotka kuvaavat vain hetkellistä stressitasoa ja joihin voidaan vastata liian myöhään ennaltaehkäisyn kannalta. Kyselyt ovat myös alttiita subjektiivisille epätarkkuuksille, koska stressintunteen, ja stressinaikaisten fysiologisten reaktioiden, on havaittu olevan yksilöllisiä. Reaaliaikaisia, biosignaalien kuten sykevälivaihtelun analyysiin perustuvia, stressintunnistimia on olemassa, mutta pääosin ne käyttävät ohjatun oppimisen menetelmiä, mikä vaatii jokaiselta järjestelmän käyttäjältä suuren stressintunteella merkityn aineiston. Stressintunnistimia myös usein testataan tilanteissa, joissa stressi on tahallisesti aiheutettua (esimerkiksi laboratoriossa). Siten ne eivät yleisty tosielämän tarpeisiin, jolloin voidaan käyttää yleisempää käyttäytymistä kuvaavaa aineistoa. Tässä tutkimuksessa vastataan datan merkintäongelmaan sekä yksilöllisyyden huomioimiseen käyttäen ohjaamattoman oppimisen stressintunnistusmalleja eri yksilöimisen tasoilla. Käytetty menetelmä, itseorganisoituva kartta, yhdistetään eri ryhmittelyalgoritmeihin tavoitteena löytää henkilökohtaiset, osin henkilökohtaiset sekä yleiset käyttäytymismallit, jotka muunnetaan stressiennusteiksi. Menetelmän sopivuuden vahvistamiseksi käytetään laboratoriossa kerättyä biosignaalidataa. Menetelmää sovelletaan myös tosielämän stressintunnistukseen biosignaaleista ja älypuhelimen käyttödatasta koostuvalla käyttäytymisaineistolla. Tulokset osoittavat, että yksilöiminen parantaa ennustetarkkuutta. Laboratorio-aineistolla paras luokittelutarkkuus löydettiin täysin yksilöllisellä mallilla (F1-pistemäärä 0.89, kun yleisellä 0.45). Tosielämän aineistolla täysin yksilöllisen (F1-pistemäärä 0.57) ja yleisen mallin, jossa käyttäytymismallien ja stressin välinen kuvaus määrättiin yksilöidysti (F1-pistemäärä 0.60), välinen ero ei ollut suuri. Vaikka tulokset vahvistavatkin itseorganisoituvan kartan sopivuuden psyykkisen stressin tunnistamisessa, lisätutkimusta tarvitaan määräämään soveltuvin ja käytännöllisin yksilöimisen taso sekä yksikäsitteinen kuvaus käyttäytymismallien ja stressin välille.

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Books on the topic "Self-organizing map"

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Klaus, Obermayer, and Sejnowski Terrence J, eds. Self-organizing map formation: Foundations of neural computation. Cambridge, Mass: MIT Press, 2001.

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Sahai, A. K. An objective study of Indian summer monsoon variability using the self organizing map algorithms. Pune: Indian Institute of Tropical Meteorology, 2006.

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United States. National Aeronautics and Space Administration., ed. Control of the NASA Langley 16-foot transonic tunnel with the self-organizing feature map. [Washington, DC: National Aeronautics and Space Administration, 1998.

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United States. National Aeronautics and Space Administration., ed. Control of the NASA Langley 16-foot transonic tunnel with the self-organizing feature map. [Washington, DC: National Aeronautics and Space Administration, 1998.

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United States. National Aeronautics and Space Administration., ed. Control of the NASA Langley 16-foot transonic tunnel with the self-organizing feature map. [Washington, DC: National Aeronautics and Space Administration, 1998.

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United States. National Aeronautics and Space Administration., ed. CONTROL OF THE NASA LANGLY 16-FOOT TRANSONIC TUNNEL WITH THE SELF-ORGANIZING FEATURE MAP... NASA/TM-98-206722... FEB. 25, 1998. [S.l: s.n., 1999.

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Kohonen, Teuvo. Self-organizing maps. Berlin: Springer, 1995.

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Kohonen, Teuvo. Self-organizing maps. 2nd ed. Berlin: Springer, 1997.

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Self-organizing maps. 3rd ed. Berlin: Springer, 2001.

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Tokutaka, Heizō. Jiko soshikika mappu to sono ōyō. Tōkyō-to Chiyoda-ku: Maruzen Kabushiki Kaisha, 2012.

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Book chapters on the topic "Self-organizing map"

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Lucchini, Mario, and Davide Bussi. "Self-Organizing Map." In Encyclopedia of Quality of Life and Well-Being Research, 1–5. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-69909-7_104673-1.

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Lucchini, Mario, and Davide Bussi. "Self-Organizing Map." In Encyclopedia of Quality of Life and Well-Being Research, 6257–61. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_104673.

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Grigore, Ovidiu. "Syntactical Self-Organizing Map." In Computational Intelligence Theory and Applications, 101–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62868-1_103.

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Attik, Mohammed, Laurent Bougrain, and Frédéric Alexandre. "Self-organizing Map Initialization." In Artificial Neural Networks: Biological Inspirations – ICANN 2005, 357–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550822_56.

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Shekhar, Shashi, and Hui Xiong. "Self Organizing Map Usage." In Encyclopedia of GIS, 1042. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1182.

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Neuland, Michaela, Mehdi Amirijoo, and Thomas Kürner. "Appendix B: X-Map Estimation for LTE." In Self-Organizing Networks, 273–77. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119954224.app2.

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Honkela, Timo, Jorma Laaksonen, Hannele Törrö, and Juhani Tenhunen. "Media Map: A Multilingual Document Map with a Design Interface." In Advances in Self-Organizing Maps, 247–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21566-7_25.

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Yu, Dongjun, Jun Hu, Xiaoning Song, Yong Qi, and Zhenmin Tang. "Supervised Kernel Self-Organizing Map." In Intelligent Science and Intelligent Data Engineering, 246–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36669-7_31.

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Sarlin, Peter. "Self-Organizing Financial Stability Map." In Computational Risk Management, 159–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54956-4_7.

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Yu, Dongjun, Edwin R. Hancock, and William A. P. Smith. "A Riemannian Self-Organizing Map." In Image Analysis and Processing – ICIAP 2009, 229–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_26.

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Conference papers on the topic "Self-organizing map"

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Dozono, Hiroshi, Gen Niina, and Satoru Araki. "Convolutional Self Organizing Map." In 2016 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2016. http://dx.doi.org/10.1109/csci.2016.0149.

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Chow, Chi Kin, and Shiu Yin Yuen. "Signal Self Organizing Map." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4370957.

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Wu, Yingxin, and Masahiro Takatsuka. "Geodesic self-organizing map." In Electronic Imaging 2005, edited by Robert F. Erbacher, Jonathan C. Roberts, Matti T. Grohn, and Katy Borner. SPIE, 2005. http://dx.doi.org/10.1117/12.586807.

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Dozono, Hiroshi, Ryuhei Matsuo, and Koki Yoshioka. "Reservoir Self Organizing Map." In 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS). IEEE, 2022. http://dx.doi.org/10.1109/scisisis55246.2022.10001926.

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"ASSOCIATIVE SELF-ORGANIZING MAP." In International Conference on Neural Computation. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002318403630370.

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Kumar, D. Indra, and Manjunath R. Kounte. "Comparative study of self-organizing map and deep self-organizing map using MATLAB." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754303.

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Shang, Qing-Zhen, and Hong-Jie Xing. "Correntropy based self-organizing map." In 2016 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2016. http://dx.doi.org/10.1109/icmlc.2016.7860929.

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Yeloglu, Ozge, A. Nur Zincir-Heywood, and Malcolm I. Heywood. "Growing recurrent self organizing map." In 2007 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icsmc.2007.4414001.

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Ayu, Aqila Dzikra, Hansel Matthew, Iman Herlambang Suherman, Aries Subiantoro, and Benyamin Kusumoputro. "Development of Autonomous Control System using Self-Organizing Map and Autoregressive Self-Organizing Map." In 2022 9th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2022. http://dx.doi.org/10.23919/eecsi56542.2022.9946634.

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Lebbah, Mustapha, Nicoleta Rogovschi, and Younes Bennani. "BeSOM : Bernoulli on Self-Organizing Map." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371030.

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Reports on the topic "Self-organizing map"

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Eguchi, Junji, and Manabu Murakami. Application of Self-Organizing Map to Inspection Technology for Gear Surface. Warrendale, PA: SAE International, September 2005. http://dx.doi.org/10.4271/2005-08-0575.

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Herrera, Allen, and Alexander Heifetz. Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms - Consortium on Nuclear Security Technologies (CONNECT) Q1 Report. Office of Scientific and Technical Information (OSTI), December 2021. http://dx.doi.org/10.2172/1841591.

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Ortiz, M. Growing Self-Organizing Maps as Predictors for Photometric Redshift. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1557954.

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Ponmalai, R., and C. Kamath. Self-Organizing Maps and Their Applications to Data Analysis. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1566795.

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Soroko, Nataliia V., Lorena A. Mykhailenko, Olena G. Rokoman, and Vladimir I. Zaselskiy. Educational electronic platforms for STEAM-oriented learning environment at general education school. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3884.

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The article is devoted to the problem of the use of educational electronic platform for the organization of a STEAM-oriented environment of the general school. The purpose of the article is to analyze the use of educational electronic platforms for organizing the STEAM-oriented school learning environment and to identify the basic requirements for supporting the implementation and development of STEAM education in Ukraine. One of the main trends of education modernization is the STEAM education, which involves the integration between the natural sciences, the technological sciences, engineering, mathematics and art in the learning process of educational institutions, in particular, general school. The main components of electronic platform for education of the organization STEAM-oriented educational environment should be open e-learning and educational resources that include resources for students and resources for teachers; information and communication technologies that provide communication and collaboration among students; between teachers; between students and teachers; between specialists, employers, students, and teachers; information and communication technologies that promote the development of STEAM education and its implementation in the educational process of the school; online assessment and self-assessment of skills and competences in STEAM education and information and communication technologies fields; STEAM education labs that may include simulators, games, imitation models, etc.; STEAM-oriented educational environment profiles that reflect unconfirmed participants’ data, their contributions to projects and STEAM education, plans, ideas, personal forums, and more. Prospects for further research are the design of an educational electronic platform for the organization of the STEAM-oriented learning environment in accordance with the requirements specified in the paper.

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Khairulin, Alexander, Vladimir Blinkov, Lyubov Lagunova, Olga Sapozhnikova, Evgeny Byzov, Irina Freifeld, Oleg Malozemov, et al. Electronic training course "Theoretical foundations of physical culture". SIB-Expertise, January 2024. http://dx.doi.org/10.12731/er0788.29012024.

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The electronic training course “Theoretical Foundations of Physical Culture” contains fifteen topics that allow students to understand: the basic concepts of physical culture (PC) and sports; its role in general cultural and professional training; socio-biological foundations of PC; the role of motor activity for modern man; the basics of a healthy lifestyle; means of PC in the regulation of life activity; basics of general physical and sports training; the basics of the theory and methodology of physical education; the basics of methods for organizing and conducting independent physical education and recreational activities; the basics of self-control and functional diagnostics during physical education classes; professional and applied physical training of university students; basics of health PC; doping problems in sports activities; the basics of massage and its connection with PC; information on sports injuries; basics of adaptive PC The content of each topic includes a lecture part, designed in the form of an illustrated test with hyperlinks, a presentation on the lecture, a practical assignment on the material of the topic, as well as a test assignment (of ten questions) on the topic. The final control of students' completion of the course is carried out by sequential study of topics (all or several, depending on the curriculum of the specialty) and the accumulation of at least 70% of correct answers on control tests. In addition to the lecture material and practical assignments, the electronic training course contains information necessary for students to master the educational material in a high-quality manner, in the form of additional reference (glossary) and educational literature previously published by the authors of this course. The structure and content of the electronic course complies with the requirements of the Federal State Educational Standard for the academic discipline “Physical Culture and Sports”, as well as the requirements of the USMU for the structure and content for this educational tool.

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