Log in

Relevant bibliographies by topics / Self-organizing map / Journal articles

Journal articles on the topic 'Self-organizing map'

To see the other types of publications on this topic, follow the link: Self-organizing map.

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Self-organizing map.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

3

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Yasunaga, Moritoshi. "Hardware Implementation of Self-Organizing Map." Journal of The Japan Institute of Electronics Packaging 23, no. 2 (March 1, 2020): 128–34. http://dx.doi.org/10.5104/jiep.23.128.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Kuo, Huang-Cheng, and Shih-Hao Chen. "Self-Organizing Map Learning with Momentum." Computer and Information Science 9, no. 1 (January 31, 2016): 136. http://dx.doi.org/10.5539/cis.v9n1p136.

Full text

Abstract:

<p class="zhengwen"><span lang="EN-GB">Self-organizing map (SOM) is a type of artificial neural network for cluster analysis. Each neuron in the map competes with others for the input data objects in order to learn the grouping of the input space. Besides competition, neighbor neurons of a winning neuron also learn. SOM has a natural propensity to cluster data into visually distinct clusters, which show the intrinsic grouping of data.</span></p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">The self-organizing map algorithm is heuristic in nature and will almost always converge. Since self-organizing map may be trapped in a local optimum, so we introduce momentum into the learning process thus the movement of a neuron may jump over local optimum. We expect this will be similar to the learning of neurons in back-propagation with momentum. Like the learning process in back-propagation, the timing for updating the amount of movement of a neuron is either batch mode or incremental mode. However, due to the neighborhood function, the movement of a non-winner neuron is relatively small as compare to when it is a winner. So when deciding the momentum, the previous movement of a neuron needs special consideration. Experiment result show that adding momentum to self-organizing map considerably contributes to the acceleration of the convergence.</span>

APA, Harvard, Vancouver, ISO, and other styles

13

Vesanto, J., and E. Alhoniemi. "Clustering of the self-organizing map." IEEE Transactions on Neural Networks 11, no. 3 (May 2000): 586–600. http://dx.doi.org/10.1109/72.846731.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Mu-Chun Su and Hsiao-Te Chang. "Fast self-organizing feature map algorithm." IEEE Transactions on Neural Networks 11, no. 3 (May 2000): 721–33. http://dx.doi.org/10.1109/72.846743.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Kittiwachana, Sila, Diana L. S. Ferreira, Louise A. Fido, Duncan R. Thompson, Richard E. A. Escott, and Richard G. Brereton. "Self-Organizing Map Quality Control Index." Analytical Chemistry 82, no. 14 (July 15, 2010): 5972–82. http://dx.doi.org/10.1021/ac100383g.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Kohonen, Teuvo. "Essentials of the self-organizing map." Neural Networks 37 (January 2013): 52–65. http://dx.doi.org/10.1016/j.neunet.2012.09.018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Ferles, Christos, Yannis Papanikolaou, and Kevin J. Naidoo. "Denoising Autoencoder Self-Organizing Map (DASOM)." Neural Networks 105 (September 2018): 112–31. http://dx.doi.org/10.1016/j.neunet.2018.04.016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Yang, Miin-Shen, Wen-Liang Hung, and De-Hua Chen. "Self-organizing map for symbolic data." Fuzzy Sets and Systems 203 (September 2012): 49–73. http://dx.doi.org/10.1016/j.fss.2012.04.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Berglund, E., and J. Sitte. "The Parameterless Self-Organizing Map Algorithm." IEEE Transactions on Neural Networks 17, no. 2 (March 2006): 305–16. http://dx.doi.org/10.1109/tnn.2006.871720.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Cheung, Yiu-ming, and Lap-tak Law. "Rival-Model Penalized Self-Organizing Map." IEEE Transactions on Neural Networks 18, no. 1 (January 2007): 289–95. http://dx.doi.org/10.1109/tnn.2006.885039.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

MASUDA, Masato, Yasushi NAKABAYASHI, and Genki YAGAWA. "Radius Parallel Self-Organizing Map (RPSOM)." Journal of Computational Science and Technology 6, no. 1 (2012): 16–27. http://dx.doi.org/10.1299/jcst.6.16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

TAMURA, Yoshiaki, and Masato MASUDA. "Flow Visualization Using Self-Organizing Map." Proceedings of The Computational Mechanics Conference 2022.35 (2022): 16–17. http://dx.doi.org/10.1299/jsmecmd.2022.35.16-17.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Özçift, Akın, Mehmet Kaya, Arif Gülten, and Mustafa Karabulut. "Swarm optimized organizing map (SWOM): A swarm intelligence basedoptimization of self-organizing map." Expert Systems with Applications 36, no. 7 (September 2009): 10640–48. http://dx.doi.org/10.1016/j.eswa.2009.02.051.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Lee, Hyung-Woo, and Jong-Won Seo. "Detection Mechanism of Attacking Web Service DoS using Self-Organizing Map." Journal of the Korea Contents Association 8, no. 5 (May 31, 2008): 9–18. http://dx.doi.org/10.5392/jkca.2008.8.5.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Han, Soo-Whan. "Self-Organizing Map for Blind Channel Equalization." Journal of information and communication convergence engineering 8, no. 6 (December 31, 2010): 609–17. http://dx.doi.org/10.6109/jicce.2010.8.6.609.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

ZHENG, Hui-Cheng, and Wei SHEN. "A Localized Linear Manifold Self-organizing Map." Acta Automatica Sinica 34, no. 10 (April 7, 2009): 1298–304. http://dx.doi.org/10.3724/sp.j.1004.2008.01298.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Sakkari, Mohamed, Monia Hamdi, Hela Elmannai, Abeer AlGarni, and Mourad Zaied. "Feature Extraction-Based Deep Self-Organizing Map." Circuits, Systems, and Signal Processing 41, no. 5 (January 15, 2022): 2802–24. http://dx.doi.org/10.1007/s00034-021-01914-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Żochowski, Michał, and Larry S. Liebovitch. "Self-organizing dynamics of coupled map systems." Physical Review E 59, no. 3 (March 1, 1999): 2830–37. http://dx.doi.org/10.1103/physreve.59.2830.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

TOKUNAGA, Kazuhiro. "Modular Network for Generalized Self-Organizing Map." IEICE ESS Fundamentals Review 14, no. 2 (October 1, 2020): 97–106. http://dx.doi.org/10.1587/essfr.14.2_97.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Primandari, Arum Handini, and Nur Aini Ikasakti. "Job applicants clustering using self-organizing map." Bulletin of Social Informatics Theory and Application 1, no. 2 (December 1, 2017): 60–71. http://dx.doi.org/10.31763/businta.v1i2.28.

Full text

Abstract:

Yogyakarta Government through Directorate of Manpower and Transmigration (Disnakertrans) have been canvassing people looking for job. An employment program was provided by Disnakertrans to allow job applicants meet companies. This research was carried out to identify educational background of applicants, in order to obtain the suitable worker. One of the ways to identify educational background is by district clustering in Yogyakarta. Clustering method is employed to reveal the characteristic of educational quality in every district in Yogyakarta. Clustering is a grouping method which is done by minimalize the characteristic among class members and minimalize the characteristic among clusters. This research used Self Organizing Maps to grouping districts in Yogyakarta according to educational background of its job seekers. The clustering results 3 clusters: 6 districts belong to cluster 1, 4 districts belong to cluster 2, and 4 districts belong to cluster 3. Then, Yogyakarta map is used to visualize the result of district clustering.

APA, Harvard, Vancouver, ISO, and other styles

31

NISHIYAMA, Koji, Shinichi ENDO, and Kenji JINNO. "RAINFALL PREDICTION BASED ON SELF-ORGANIZING MAP." PROCEEDINGS OF HYDRAULIC ENGINEERING 50 (2006): 403–8. http://dx.doi.org/10.2208/prohe.50.403.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Similä, Timo. "Self-Organizing Map Learning Nonlinearly Embedded Manifolds." Information Visualization 4, no. 1 (March 2005): 22–31. http://dx.doi.org/10.1057/palgrave.ivs.9500088.

Full text

Abstract:

One of the main tasks in exploratory data analysis is to create an appropriate representation for complex data. In this paper, the problem of creating a representation for observations lying on a low-dimensional manifold embedded in high-dimensional coordinates is considered. We propose a modification of the Self-organizing map (SOM) algorithm that is able to learn the manifold structure in the high-dimensional observation coordinates. Any manifold learning algorithm may be incorporated to the proposed training strategy to guide the map onto the manifold surface instead of becoming trapped in local minima. In this paper, the Locally linear embedding algorithm is adopted. We use the proposed method successfully on several data sets with manifold geometry including an illustrative example of a surface as well as image data. We also show with other experiments that the advantage of the method over the basic SOM is restricted to this specific type of data.

APA, Harvard, Vancouver, ISO, and other styles

33

Kohonen, T., E. Oja, O. Simula, A. Visa, and J. Kangas. "Engineering applications of the self-organizing map." Proceedings of the IEEE 84, no. 10 (1996): 1358–84. http://dx.doi.org/10.1109/5.537105.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Obayashi, Shigeru. "Design Datamining Based on Self-Organizing Map." Proceedings of the JSME annual meeting 2002.7 (2002): 23–24. http://dx.doi.org/10.1299/jsmemecjo.2002.7.0_23.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Gunawan, D., D. Arisandi, F. M. Ginting, R. F. Rahmat, and A. Amalia. "Russian Character Recognition using Self-Organizing Map." Journal of Physics: Conference Series 801 (January 2017): 012040. http://dx.doi.org/10.1088/1742-6596/801/1/012040.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Velzen, G. A. van. "Instabilities in Kohonen's self-organizing feature map." Journal of Physics A: Mathematical and General 27, no. 5 (March 7, 1994): 1665–81. http://dx.doi.org/10.1088/0305-4470/27/5/029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

López-Rubio, Ezequiel, José Muñoz-Pérez, and José Antonio Gómez-Ruiz. "A principal components analysis self-organizing map." Neural Networks 17, no. 2 (March 2004): 261–70. http://dx.doi.org/10.1016/j.neunet.2003.04.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Rynkiewicz, Joseph. "Self-organizing map algorithm and distortion measure." Neural Networks 19, no. 6-7 (July 2006): 830–37. http://dx.doi.org/10.1016/j.neunet.2006.05.016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Ferles, Christos, and Andreas Stafylopatis. "Self-Organizing Hidden Markov Model Map (SOHMMM)." Neural Networks 48 (December 2013): 133–47. http://dx.doi.org/10.1016/j.neunet.2013.07.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

López-Rubio, Ezequiel, and Antonio Díaz Ramos. "Grid topologies for the self-organizing map." Neural Networks 56 (August 2014): 35–48. http://dx.doi.org/10.1016/j.neunet.2014.05.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Allahyar, Amin, Hadi Sadoghi Yazdi, and Ahad Harati. "Constrained Semi-Supervised Growing Self-Organizing Map." Neurocomputing 147 (January 2015): 456–71. http://dx.doi.org/10.1016/j.neucom.2014.06.039.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Shah-Hosseini, Hamed. "Binary tree time adaptive self-organizing map." Neurocomputing 74, no. 11 (May 2011): 1823–39. http://dx.doi.org/10.1016/j.neucom.2010.07.037.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Sarlin, Peter, and Zhiyuan Yao. "Clustering of the Self-Organizing Time Map." Neurocomputing 121 (December 2013): 317–27. http://dx.doi.org/10.1016/j.neucom.2013.04.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Yang, Zheng Rong, and Kuo-Chen Chou. "Mining Biological Data Using Self-Organizing Map." Journal of Chemical Information and Computer Sciences 43, no. 6 (November 2003): 1748–53. http://dx.doi.org/10.1021/ci034138n.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Mehrizi, Ali, Hadi Sadoghi Yazdi, and Amir Hossein Taherinia. "Robust Semi-Supervised Growing Self-Organizing Map." Expert Systems with Applications 105 (September 2018): 23–33. http://dx.doi.org/10.1016/j.eswa.2018.03.046.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Ruz, Gonzalo A., and Duc Truong Pham. "NBSOM: The naive Bayes self-organizing map." Neural Computing and Applications 21, no. 6 (March 2, 2011): 1319–30. http://dx.doi.org/10.1007/s00521-011-0567-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Villmann, Th, and H. U. Bauer. "Applications of the growing self-organizing map." Neurocomputing 21, no. 1-3 (November 1998): 91–100. http://dx.doi.org/10.1016/s0925-2312(98)00037-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Kiviluoto, Kimmo. "Predicting bankruptcies with the self-organizing map." Neurocomputing 21, no. 1-3 (November 1998): 191–201. http://dx.doi.org/10.1016/s0925-2312(98)00038-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Hsu, C. C. "Generalizing Self-Organizing Map for Categorical Data." IEEE Transactions on Neural Networks 17, no. 2 (March 2006): 294–304. http://dx.doi.org/10.1109/tnn.2005.863415.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Hovakimyan, Anna Sedrak, Siranush Gegham Sargsyan, and Arshak Nazaryan. "Self-Organizing Map Application for Iris Recognition." Journal of Communications and Computer Engineering 3, no. 2 (March 1, 2014): 10. http://dx.doi.org/10.20454/jcce.2013.760.

Full text

Abstract:

Human iris is a good subject of biometrical identification, since iris patterns are unique like fingerprints. Iris is well protected against damage, unlike fingerprints, which can be harder to recognize after years of certain types of manual labor.A problem of iris recognition is considered in the paper. In machine learning, pattern recognition is the assignment of a label to a given input value. Pattern classification is an example of pattern recognition: it attempts to assign each input value to one of a given set of classes. Nowadays various techniques are used for this purpose, and in particular artificial neural networks.For iris recognition problem solving Kohenen Self Organizing Maps are suggested to use. The software for iris recognition is developed which is customizable and allows to select the appropriate parameters of the neural network to obtain the most satisfactory results. The developed Self-Organizing Map Library of classes can be used for various kinds of object classification problem solving as well as for any problems suitable to solve with Self-Organizing Maps.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!