Relevant bibliographies by topics / Kohonen self-organized maps

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Kohonen self-organized maps'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Kohonen self-organized maps"

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Wypart, Krystian, and Edyta Łukasik. "Impact of metrics on the effectiveness of Kohonen network clustering." Journal of Computer Sciences Institute 34 (March 30, 2025): 1–7. https://doi.org/10.35784/jcsi.6272.

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The research paper focuses on investigating the impact of different metrics on the clustering process in Kohonen networks, also known as self-organized maps (SOM). The theoretical foundations of Kohonen networks, including their structure and algorithm of operation, are presented. Various metrics, such as Euclidean distance, Manhattan and cosine distance and their potential impact on clustering process are then discussed. Experiments were conducted using the Iris dataset constrained to two dimensions using PCA.
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Low, Kian Hsiang, Wee Kheng Leow, and Marcelo H. Ang. "An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks." Neural Computation 17, no. 6 (2005): 1411–45. http://dx.doi.org/10.1162/0899766053630378.

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Self-organizing feature maps such as extended Kohonen maps (EKMs) have been very successful at learning sensorimotor control for mobile robot tasks. This letter presents a new ensemble approach, cooperative EKMs with indirect mapping, to achieve complex robot motion. An indirect-mapping EKM self-organizes to map from the sensory input space to the motor control space indirectly via a control parameter space. Quantitative evaluation reveals that indirect mapping can provide finer, smoother, and more efficient motion control than does direct mapping by operating in a continuous, rather than disc
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Gustafsson, Lennart. "A Case of Near-Optimal Sensory Integration Based on Kohonen Self-Organizing Maps." Neural Computation 31, no. 7 (2019): 1419–29. http://dx.doi.org/10.1162/neco_a_01200.

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This letter shows by digital simulation that a simple rule applied to one-dimensional self-organized maps for integrating sensory perceptions from two identical sources yielding position information as integers, corrupted by independent noise sources, yields almost statistically optimal results for position estimation as determined by maximum likelihood estimation. There is no learning of the corrupting noise sources nor is any information about the statistics of the noise sources available to the integrating process. The simple rule employed yields a measure of the quality of the estimated po
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Kasabov, Nikola, and Robert Kozma. "Self-Organization and Adaptation in Intelligent Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 6 (1998): 177. http://dx.doi.org/10.20965/jaciii.1998.p0177.

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This special issue is devoted to one of the important topics of current intelligent information systems-their ability to adapt to the environment they operate in, as adaptation is one of the most important features of intelligence. Several milestones in the literature on adaptive systems mark the development in this area. The Hebbian learning rule,1) self-organizing maps,2,3) and adaptive resonance theory4) have influenced the research in this area a great deal. Some current development suggests methods for building adaptive neurofuzzy systems,5) and adaptive self-organizing systems based on p
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Leinonen, Lea, Jari Kangas, Kari Torkkola, and Anja Juvas. "Dysphonia Detected by Pattern Recognition of Spectral Composition." Journal of Speech, Language, and Hearing Research 35, no. 2 (1992): 287–95. http://dx.doi.org/10.1044/jshr.3502.287.

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The vowel [a:] in a test word, judged normal or dysphonic, was examined with the Self-Organizing Map, the artificial neural network algorithm of Kohonen. The algorithm produces two-dimensional representations (maps) of speech. Input to the acoustic maps consisted of 15-component spectral vectors calculated at 9.83-msec intervals from short-time power spectra. The male and female maps were first calculated from the speech of healthy subjects and then the [a:] samples (15 successive spectral vectors) were examined on the maps. The dysphonic voices deviated from the norm both in the composition o
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Lin, Juan K., David G. Grier, and Jack D. Cowan. "Faithful Representation of Separable Distributions." Neural Computation 9, no. 6 (1997): 1305–20. http://dx.doi.org/10.1162/neco.1997.9.6.1305.

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A geometric approach to data representation incorporating information theoretic ideas is presented. The task of finding a faithful representation, where the input distribution is evenly partitioned into regions of equal mass, is addressed. For input consisting of mixtures of statistically independent sources, we treat independent component analysis (ICA) as a computational geometry problem. First, we consider the separation of sources with sharply peaked distribution functions, where the ICA problem becomes that of finding high-density directions in the input distribution. Second, we consider
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Stoica, Alina, Thomas Couronne, and Jean Samuel Beuscart. "To Be a Star Is Not Only Metaphoric: From Popularity to Social Linkage." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (2010): 154–61. http://dx.doi.org/10.1609/icwsm.v4i1.14012.

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The emergence of online platforms allowing to mix self publishing activities and social networking offers new possibilities for building online reputation and visibility. In this paper we present a method to analyze the online popularity that takes into consideration both the success of the published content and the social network topology. First, we adapt the Kohonen self organizing maps in order to cluster the users of online platforms depending on their audience and authority characteristics. Then, we perform a detailed analysis of the manner nodes are organized in the social network. Final
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Kuzior, Aleksandra, Inna Tiutiunyk, Anetta Zielińska, and Roland Kelemen. "Cybersecurity and cybercrime: Current trends and threats." JOURNAL OF INTERNATIONAL STUDIES 17, no. 2 (2024): 220–39. http://dx.doi.org/10.14254/2071-8330.2024/17-2/12.

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The rapid development of digital technologies and their active implementation in all spheres of the economy, on the one hand, contribute to increased control over the activities of economic entities, and on the other hand, create new challenges associated with the dynamic development of cybercrime. The purpose of the article is to analyze the global trends in cybercrime in 2016-2023 (by calculating national levels of cybercrime) and to model the mechanisms of combating it in order to form a global, open and safe cyberspace, protect the population from cyber threats and cyber fraud, increase th
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Zabolotsky, A. A. "The Study on Knowledge Self-Organization and Technological Structure of the Russian Regions by Means of Kohonen’s Self-Organizing Maps." World of Economics and Management 21, no. 2 (2021): 73–102. http://dx.doi.org/10.25205/2542-0429-2021-21-2-73-102.

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The article proposes a novel model for assessing the quality of technological development which differs from the similar spillovers by introducing a fundamentally new parameter of knowledge selforganization. Unlike spillovers measuring financial R&D flows, knowledge spillover measures structural similarities presented in patents, articles and other quantized units. Being the results of the reactions on the emergence of technological tasks and absorbing new technologies themselves, patents reflect real industrial picture of distribution of new technologies in any particular area. Implementa
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Ossowska, Agata, Aida Kusiak, and Dariusz Świetlik. "Progression of Selected Parameters of the Clinical Profile of Patients with Periodontitis Using Kohonen’s Self-Organizing Maps." Journal of Personalized Medicine 13, no. 2 (2023): 346. http://dx.doi.org/10.3390/jpm13020346.

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(1) Background: Periodontitis is an inflammatory condition that affects the tissues surrounding the tooth and causes clinical attachment loss, which is the loss of periodontal attachment (CAL). Periodontitis can advance in various ways, with some patients experiencing severe periodontitis in a short period of time while others may experience mild periodontitis for the rest of their lives. In this study, we have used an alternative methodology to conventional statistics, self-organizing maps (SOM), to group the clinical profiles of patients with periodontitis. (2) Methods: To predict the period
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Dissertations / Theses on the topic "Kohonen self-organized maps"

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Гриценко, Костянтин Григорович, Константин Григорьевич Гриценко та Kostiantyn Hryhorovych Hrytsenko. "Моделювання динаміки розвитку страхових компаній на основі динамічного аналізу патернів". Thesis, Черкаси: Видавець Ольга Вовчок, 2017. https://essuir.sumdu.edu.ua/handle/123456789/80612.

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Динамічний аналіз патернів - нова область аналізу даних, пов'язана з вивченням динаміки розвитку соціально-економічних об'єктів, виявленням їх взаємозалежності та класифікацією. Запропоновано науково-методичний підхід до моделювання динаміки розвитку страхувальників життя на основі динамічного аналізу патернів<br>Динамический анализ паттернов - новое направление анализа данных, связанное с изучением динамики развития социально-экономических объектов, поиском их взаимозависимостей и классификации. Предлагается научно-методический подход к моделированию динамики развития страховщиков жизни, осно
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Book chapters on the topic "Kohonen self-organized maps"

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"SELF-ORGANIZED FEATURE MAPS: THE KOHONEN MODEL." In Physical Models of Neural Networks. WORLD SCIENTIFIC, 1990. http://dx.doi.org/10.1142/9789814434492_0010.

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Coolen, A. C. C., R. Kühn, and P. Sollich. "Notes and suggestions for further reading." In Theory of Neural Information Processing Systems. Oxford University PressOxford, 2005. http://dx.doi.org/10.1093/oso/9780198530237.003.0009.

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Abstract In Chapter 5 we dealt with some unsupervised learning techniques, namely vector quantization and self-organized maps. The books by Kohonen [26] and by Ritter et al. [27] are good sources of further information on these; much of the early work on self-organizing maps was done in particular by Amari [28] and Kohonen [29]. We saw for vector quantization that this can be thought as learning a probability distribution from data. This point of view is rather powerful, and in fact lets one integrate unsupervised learning with Bayesian techniques: different probability models for the data can
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"(1982) Teuvo Kohonen, "Self-organized formation of topologically correct feature maps," [i]Biological Cybernetics[/i] 43:59-69." In Neurocomputing, Volume 1. The MIT Press, 1988. http://dx.doi.org/10.7551/mitpress/4943.003.0031.

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Conference papers on the topic "Kohonen self-organized maps"

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Akinin, Maxim V., Alexandr I. Taganov, Michael B. Nikiforov, and Alexandra V. Sokolova. "Image segmentation algorithm based on self-organized Kohonen's neural maps and tree pyramidal segmenter." In 2015 4th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2015. http://dx.doi.org/10.1109/meco.2015.7181893.

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Sawant, Pravin, Joshua Schelegel, Sidharth Paranjape, Basar Ozar, Takashi Hibiki, and Mamoru Ishii. "Flow Regime Identification in Large Diameter Pipe." In 16th International Conference on Nuclear Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/icone16-48311.

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Air-water vertical two-phase flow experiments were performed in a 0.15 m diameter and 4.4 m long test section. Superficial liquid velocity was varied from 0.05 m/s to 2.0 m/s and superficial gas velocity was varied to obtain the area averaged void fraction range of 0.1 to 0.7. Exit pressure was close to the atmospheric pressure. In order to study the development of flow structure over the length of test section, area averaged void fraction was measured using impedance meters at four different measuring ports. Pressure drop was also measured between these ports. Since the temporal variation of
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