Journal articles: 'Self-organizing maps of Kohonen'

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Termini, Settimo. "T. Kohonen,self-organizing maps." Rendiconti del Circolo Matematico di Palermo 44, no. 3 (September 1995): 506. http://dx.doi.org/10.1007/bf02844683.

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Claussen, Jens Christian. "Winner-Relaxing Self-Organizing Maps." Neural Computation 17, no. 5 (May 1, 2005): 996–1009. http://dx.doi.org/10.1162/0899766053491922.

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A new family of self-organizing maps, the winner-relaxing Kohonen algorithm, is introduced as a generalization of a variant given by Kohonen in 1991. The magnification behavior is calculated analytically. For the original variant, a magnification exponent of 4/7 is derived; the generalized version allows steering the magnification in the wide range from exponent 1/2 to 1 in the one-dimensional case, thus providing optimal mapping in the sense of information theory. The winner-relaxing algorithm requires minimal extra computations per learning step and is conveniently easy to implement.

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Santini, S. "The self-organizing field [Kohonen maps]." IEEE Transactions on Neural Networks 7, no. 6 (1996): 1415–23. http://dx.doi.org/10.1109/72.548169.

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Huh, Myung-Hoe. "Validity Study of Kohonen Self-Organizing Maps." Communications for Statistical Applications and Methods 10, no. 2 (August 1, 2003): 507–17. http://dx.doi.org/10.5351/ckss.2003.10.2.507.

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Burn, Kevin, and Geoffrey Home. "Environment classification using Kohonen self-organizing maps." Expert Systems 25, no. 2 (May 2008): 98–114. http://dx.doi.org/10.1111/j.1468-0394.2008.00441.x.

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Ambroise, Christophe, and G�rard Govaert. "Constrained clustering and Kohonen Self-Organizing Maps." Journal of Classification 13, no. 2 (September 1996): 299–313. http://dx.doi.org/10.1007/bf01246104.

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Budinich, Marco. "Sorting with Self-Organizing Maps." Neural Computation 7, no. 6 (November 1995): 1188–90. http://dx.doi.org/10.1162/neco.1995.7.6.1188.

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A self-organizing feature map (Von der Malsburg 1973; Kohonen 1984) sorts n real numbers in O(n) time apparently violating the O(n log n) bound. Detailed analysis shows that the net takes advantage of the uniform distribution of the numbers and, in this case, sorting in O(n) is possible. There are, however, an exponentially small fraction of pathological distributions producing O(n2) sorting time. It is interesting to observe that standard learning produced a smart sorting algorithm.

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Chang, Hsien-Cheng, David C. Kopaska-Merkel, and Hui-Chuan Chen. "Identification of lithofacies using Kohonen self-organizing maps." Computers & Geosciences 28, no. 2 (March 2002): 223–29. http://dx.doi.org/10.1016/s0098-3004(01)00067-x.

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Miller, A. S., and M. J. Coe. "Star/galaxy classification using Kohonen self-organizing maps." Monthly Notices of the Royal Astronomical Society 279, no. 1 (March 1, 1996): 293–300. http://dx.doi.org/10.1093/mnras/279.1.293.

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Pasa, Leandro Antonio, José Alfredo Ferreira Costa, and Marcial Guerra de Medeiros. "A Contribution to the Study of Ensemble of Self-Organizing Maps." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/592549.

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This study presents a factorial experiment to investigate the ensemble of Kohonen Self-Organizing Maps. Clusters Validity Indexes and the Mean Square Quantization Error were used as a criterion for fusing Kohonen Maps, through three different equations and four approaches. Computational simulations were performed with traditional dataset, including those with high dimensionality, not linearly separable classes, Gaussian mixtures, almost touching clusters, and unbalanced classes, from the UCI Machine Learning Repository and from Fundamental Clustering Problems Suite, with variations in map size, number of ensemble components, and the percentage of dataset bagging. The proposed method achieves a better classification than a single Kohonen Map and we applied the Wilcoxon Signed Rank Test to evidence its effectiveness.

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Kozmenko, Serhiy, Inna Shkolnyk, and Alina Bukhtiarova. "Dynamics patterns of banks evaluations on the basis of Kohonen self-organizing maps." Banks and Bank Systems 11, no. 4 (December 22, 2016): 179–92. http://dx.doi.org/10.21511/bbs.11(4-1).2016.09.

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In the research, bank patterns analysis is held on the basis of Kohonen self-organizing maps with the aim to determine further directions of bank strategies development under the influence of crisis events in Ukraine’s economy. For model practical approval, the sample of 32 banks was formed, which presents four groups of banks according to the classification determined by the National Bank of Ukraine. While constructing model, 15 indexes were used that characterize bank’s functioning efficiency. As a result of research, cluster ranking was constructed, the groups (powerful banks, stable, problem banks and banks that are in the crisis state and bankrupt state) were formed and the trajectory of bank evolution as a patterns unity, each of which characterizes the activity of bank on a definite moment of time. It gives possibility for the government regulation authority – central bank to take decisions according to the appropriateness use of regulation instruments of separate bank with the aim of saving stable banking system state in a whole, and for the clients – to evaluate bank’s reliability. Keywords: banks, banking system, economic modeling, Harrington desirability function, cluster analysis, self-organizing map, pattern of bank. JEL Classifications: G17, G21, G33

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Budinich, Marco, and John G. Taylor. "On the Ordering Conditions for Self-Organizing Maps." Neural Computation 7, no. 2 (March 1995): 284–89. http://dx.doi.org/10.1162/neco.1995.7.2.284.

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We present a geometric interpretation of ordering in self-organizing feature maps. This view provides simpler proofs of Kohonen ordering theorem and of convergence to an ordered state in the one-dimensional case. At the same time it explains intuitively the origin of the problems in higher dimensional cases. Furthermore it provides a geometric view of the known characteristics of learning in self-organizing nets.

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Deichmann, Joel I., Abdolreza Eshghi, Dominique Haughton, Selin Sayek, Nicholas Teebagy, and Heikki Topi. "Understanding Eurasian Convergence: Application Of Kohonen Self-Organizing Maps." Journal of Modern Applied Statistical Methods 5, no. 1 (May 1, 2006): 73–94. http://dx.doi.org/10.22237/jmasm/1146456420.

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Mitchison, Graeme. "A Type of Duality between Self-Organizing Maps and Minimal Wiring." Neural Computation 7, no. 1 (January 1995): 25–35. http://dx.doi.org/10.1162/neco.1995.7.1.25.

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I show here that two interpretations of neural maps are closely related. The first, due to Kohonen, sees these maps as forming by an adaptive process in response to stimuli. The second—the minimal wiring or dimension-reduction perspective—interprets the maps as the solution of a minimization problem, where the goal is to keep the “wiring” between neurons with similar receptive fields as short as possible. Recent work by Luttrell provides a bridging concept, by showing that Kohonen's algorithm can be regarded as an approximation to gradient descent on a certain functional. I show how this functional can be generalized in a way that allows it to be interpreted as a measure of wirelength.

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Du, Zhan Wei, Yong Jian Yang, Yong Xiong Sun, and Chi Jun Zhang. "Map Matching Using De-Noise Interpolation Kohonen Self-Organizing Maps." Key Engineering Materials 460-461 (January 2011): 680–86. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.680.

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In this work, we have proposed a de-noise interpolation Kohonen Self-Organizing Maps(DNIKSOM) -based method for the Map matching(MM). It has been seen that there are some problems in the MM, such as large error range of the original position information, low match accuracy and so on. Therefore, in MM problem to achieve high accuracy, it is necessary to consider the topography of roads and the requirement for match accuracy lying within the original position information in the matching process. In the present study, Kohonen Self-Organizing Maps(KSOM) in the field of pattern recognition possesses good performance. Now to get more valuable position information, A kind of de-noise algorithm for Kohonen neural network is proposed to meet the case that neural network may not be trained sufficiently with consideration for the topography of roads. And a kind of Lagrange interpolation algorithm is also proposed to meet the requirements for matching accuracy. These processes make the amended position information closer to the true value. In this application to a city’s MM, we investigate DNIKSOM’s ,KSOM’s and Centroid localization algorithm’s location performance on a original position data set. Finally, the comparison of experimental results shows that DNIKSOM has better location performance than others.

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Fonseca, Ana M., José L. Biscaya, João Aires-de-Sousa, and Ana M. Lobo. "Geographical classification of crude oils by Kohonen self-organizing maps." Analytica Chimica Acta 556, no. 2 (January 2006): 374–82. http://dx.doi.org/10.1016/j.aca.2005.09.062.

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Weinlichová, Jana, and Jiří Fejfar. "Usage of self-organizing neural networks in evaluation of consumer behaviour." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, no. 6 (2010): 625–32. http://dx.doi.org/10.11118/actaun201058060625.

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This article deals with evaluation of consumer data by Artificial Intelligence methods. In methodical part there are described learning algorithms for Kohonen maps on the principle of supervised learning, unsupervised learning and semi-supervised learning. The principles of supervised learning and unsupervised learning are compared. On base of binding conditions of these principles there is pointed out an advantage of semi-supervised learning. Three algorithms are described for the semi-supervised learning: label propagation, self-training and co-training. Especially usage of co-training in Kohonen map learning seems to be promising point of other research. In concrete application of Kohonen neural network on consumer’s expense the unsupervised learning method has been chosen – the self-organization. So the features of data are evaluated by clustering method called Kohonen maps. These input data represents consumer expenses of households in countries of European union and are characterised by 12-dimension vector according to commodity classification. The data are evaluated in several years, so we can see their distribution, similarity or dissimilarity and also their evolution. In the article we discus other usage of this method for this type of data and also comparison of our results with results reached by hierarchical cluster analysis.

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Pratama, Reza Aditya, Tiara Shafira, Faisal Ardiansyah, and RB Fajriya Hakim. "Characteristics and segmentation of social problems with kohonen self-organizing maps." Bulletin of Social Informatics Theory and Application 1, no. 1 (March 1, 2017): 1–10. http://dx.doi.org/10.31763/businta.v1i1.19.

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Indonesia is a country with a low Human Development Index, it shows the number of quality and healthy standard in Indonesia is still poor. Indonesia also have various social problems such as overcrowding, poverty, unemployment, bad education level .This problem can bring negative impact for our society like increasing of crime rate. For identification phase of social problems and crime, Indonesian government does not integrate social problems which is identified can affect the crime and use descriptive statistics only. Further diagnosis required for cases of social issues. The purpose and benefits of this research is to determine the characteristics of the social problems in Indonesia, introduce and make segmentation using Kohonen Self Organizing Map’s algorithm. Hopefully the results of this analysis can helps government for make public policy in general, specifically future policy about social problems in Indonesia. Using Kohonen algorithm effective for visualizing of high-dimensional data by reducing the dimensions of ann-dimensional input into lower dimension while maintaining its original topological relations. Based of clustering result of provinces in Indonesia, it divided into 5 group and each group has similar characteristics.

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Haese, Karin. "Kalman Filter Implementation of Self-Organizing Feature Maps." Neural Computation 11, no. 5 (July 1, 1999): 1211–33. http://dx.doi.org/10.1162/089976699300016421.

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The self-organizing learning algorithm of Kohonen and most of its extensions are controlled by two learning parameters, the learning coefficient and the width of the neighborhood function, which have to be chosen empirically because neither rules nor methods for their calculation exist. Consequently, often time-consuming parameter studies precede neighborhood-preserving feature maps of the learning data. To circumvent those lengthy numerical studies, this article describes the learning process by a state-space model in order to use the linear Kalman filter algorithm training the feature maps. Then the Kalman filter equations calculate the learning coefficient online during the training, while the width of the neighborhood function needs to be estimated by a second extended Kalman filter for the process of neighborhood preservation. The performance of the Kalman filter implementation is demonstrated on toy problems as well as on a crab classification problem. The results of crab classification are compared to those of generative topographic mapping, an alternative method to the self-organizing feature map.

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Borkowska, E. M., A. Kruk, A. Jedrzejczyk, M. Rozniecki, Z. Jablonowski, M. Traczyk, M. Constantinou, et al. "C105 Molecular subtyping of bladder cancer using Kohonen self-organizing maps." European Urology Supplements 12, no. 4 (October 2013): e1213, C105. http://dx.doi.org/10.1016/s1569-9056(13)61953-3.

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Jämsä-Jounela, S. L., M. Vermasvuori, P. Endén, and S. Haavisto. "A process monitoring system based on the Kohonen self-organizing maps." Control Engineering Practice 11, no. 1 (January 2003): 83–92. http://dx.doi.org/10.1016/s0967-0661(02)00141-7.

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Bártů, Marek. "SPEECH DISORDER ANALYSIS USING MATCHING PURSUIT AND KOHONEN SELF-ORGANIZING MAPS." Neural Network World 22, no. 6 (2012): 519–33. http://dx.doi.org/10.14311/nnw.2012.22.032.

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Brett, David R., Richard G. West, and Peter J. Wheatley. "The automated classification of astronomical light curves using Kohonen self-organizing maps." Monthly Notices of the Royal Astronomical Society 353, no. 2 (September 2004): 369–76. http://dx.doi.org/10.1111/j.1365-2966.2004.08093.x.

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Pastuhova, G. "APPLICATION OF SELF-ORGANIZING KOHONEN MAPS FOR ANALYSIS OF THE PATENT BASE." National Association of Scientists 2, no. 65 (April 15, 2021): 51–54. http://dx.doi.org/10.31618/nas.2413-5291.2021.2.65.392.

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One of the clustering technologies is considered - self-organizing Kohonen networks, bottlenecks for data analysis with similar algorithms are analyzed. The general problems of the adaptation of mathematical models and the applicability of the clustering algorithms themselves are touched upon.The classification problem is one of the most ancient problems, the essence of which is to divide the set of objects under study into homogeneous groups in a certain sense. The basis for the classification is dictated by the nature of what we are classifying, although sometimes it is necessary to take as the basis such metrics for which there are objective ways to measure them.You also need to clearly distinguish between classification and typology, the latter is much broader. Typology is understood as a method of scientific knowledge, based on the dismemberment of objects and their grouping using a generalized, idealized model or type.

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FUJIMURA, Kikuo, Heizo TOKUTAKA, and Satoru KISHIDA. "A Method of Classification Using Kohonen's Self-Organizing Feature Maps." IEEJ Transactions on Electronics, Information and Systems 115, no. 5 (1995): 736–43. http://dx.doi.org/10.1541/ieejeiss1987.115.5_736.

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Pasa, Leandro Antonio, José Alfredo F. Costa, and Marcial Guerra de Medeiros. "An ensemble algorithm for Kohonen self-organizing map with different sizes." Logic Journal of the IGPL 25, no. 6 (September 23, 2017): 1020–33. http://dx.doi.org/10.1093/jigpal/jzx046.

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Abstract Data Clustering aims to discover groups within the data based on similarities, with a minimal, if any, knowledge of their structure. Variations in the results may occur due to many factors, including algorithm parameters, initialization and stopping criteria. The usage of different attributes or even different subsets of data usually lead to different results. Self-organizing maps (SOM) has been widely used for a variety of tasks regarding data analysis, including data visualization and clustering. A machine committee, or ensemble, is a set of neural networks working independently with some system that enable the combination of individual results into a single output, with the aim to achieve a better generalization compared to a unique neural network. This article presents a new ensemble method that uses SOM networks. Cluster validity indexes are used to combine neuron weights from different maps with different sizes. Results are shown from simulations with real and synthetic data, from the UCI Repository and Fundamental Clustering Problems Suite. The proposed method presented promising results, with increased performance compared with conventional single Kohonen map.

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FORNELLS, A., J. M. MARTORELL, E. GOLOBARDES, J. M. GARRELL, and X. VILASÍS. "PATTERNS OUT OF CASES USING KOHONEN MAPS IN BREAST CANCER DIAGNOSIS." International Journal of Neural Systems 18, no. 01 (February 2008): 33–43. http://dx.doi.org/10.1142/s012906570800135x.

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DESMAI is a framework for helping experts in breast cancer diagnosis. It allows experts to explore digital mammographic image databases according to a certain topology criteria when they need to decide whether a sample is benign or malignant. In this way, they are provided with complementary information to enhance their interpretations and predictions. The core of the application is a SOMCBR system, which is variant of a Case-Based Reasoning system featured by organizing the case memory using a Self-Organizing Map. The article presents a strategy for improving the SOMCBR reliability thanks to the relations between cases and clusters. The approach is successfully applied in DESMAI for estimating, if it is possible, the class of the recovered mammographies.

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Bhowmick, Kiran, and Mansi Shah. "Kohonen's Self-Organizing Feature Maps and Linear Vector Quantization: A Comparison." International Journal of Computer Applications 122, no. 6 (July 18, 2015): 33–35. http://dx.doi.org/10.5120/21707-4823.

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Elfadil, Nazar, Mohamed Khalil Hani, Sulaiman Mohd Nor, and Sheikh Hussein. "Kohonen Self-Organizing Maps and Expert System for Network Virtual Memory Performance Prediction." Systems Analysis Modelling Simulation 42, no. 7 (January 2002): 1025–43. http://dx.doi.org/10.1080/716067205.

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Strecker, Uwe, and Richard Uden. "Data mining of 3D poststack seismic attribute volumes using Kohonen self-organizing maps." Leading Edge 21, no. 10 (October 2002): 1032–37. http://dx.doi.org/10.1190/1.1518442.

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Gustafsson, Lennart. "A Case of Near-Optimal Sensory Integration Based on Kohonen Self-Organizing Maps." Neural Computation 31, no. 7 (July 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 position of the source. The letter also shows that if the Bayesian estimates, which are rational numbers, are rounded in order to comply with the stipulation that integers be identified, the Bayesian estimation will have a larger variance than the proposed integration.

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Kolasa, Marta, Rafał Długosz, and Jolanta Pauk. "A Comparative Study of Different Neighborhood Topologies in WTM Kohonen Self-Organizing Maps." Solid State Phenomena 147-149 (January 2009): 564–69. http://dx.doi.org/10.4028/www.scientific.net/ssp.147-149.564.

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In this paper we present a software model of the Winner Takes Most (WTM) Kohonen neural network (KNN) with different types of the neighborhood grid. The proposed network model allows for analysis of the convergence properties such as the quantization error and the convergence time for different grids, which is essential looking from the hardware implementation point of view of such networks. Particular grids differ in complexity, which in hardware implementation has a direct influence on power dissipation as well as on chip area and the final production cost. The presented results show that even the simplest rectangular grid with four neighbors allows for good convergence properties for different training data files.

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Deetz, Marcus. "K-Means Clustering of Self-Organizing Maps: An Empirical Study on the Information Content of Self-Classification of Hedge Fund Managers." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 5, no. 3 (2019): 43–57. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.53.1006.

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With the implementation of the 2-step approach according to Vesanto & Alhoniemi (2000), this article extends the procedure of visual evaluation of the Kohonen Maps usually chosen in the hedge fund literature for classification with Self-Organizing Maps. It introduces an automated procedure which guarantees a consistent combination of adjacent output units and thus an objective classification. The practical application of this method results in a reduction of the strategy groups specified by the database. This is also accompanied by a significant reduction in the Davies Bouldin Index (DBI) of the SOM partitions. Since a small dispersion within the clusters and large distances between the clusters lead to small DBIs, a minimization of this measure is desired. This significantly better partitioning of SOMs in comparison to the classification of hedge funds into the categorization scheme specified by the database provider can be observed in all examined data samples (robustness analyses). Ultimately, none of the original 23 strategy groups can be empirically validated. Furthermore, no stable classification can be found. Both the number of empirically determined categories (SOM clusters) and the composition of these clusters differ significantly in the subsamples examined. Thus the results essentially confirm the results and conclusions in the literature, according to which the original, self-classified strategy labels of the database providers are misleading and therefore do not contain any information content.

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Mints, Oleksii, Viktoriya Marhasova, Hanna Hlukha, Roman Kurok, and Tetiana Kolodizieva. "Analysis of the stability factors of Ukrainian banks during the 2014–2017 systemic crisis using the Kohonen self-organizing neural networks." Banks and Bank Systems 14, no. 3 (September 6, 2019): 86–98. http://dx.doi.org/10.21511/bbs.14(3).2019.08.

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The article proposes an approach to analyzing reliability factors of commercial banks during the 2014–2017 systemic crisis in the Ukrainian banking system, using the Kohonen self-organizing neural networks and maps. As a result of an experimental study, data were obtained on financial factors affecting the stability of a commercial bank in a crisis period. It has been concluded that during the banking crisis in Ukraine in 2014–2017, the resource base of a bank was the main factor of this bank stability. The most preferred sources of resources were funds from other banks (bankruptcy rate of 5.7%) and legal entities (bankruptcy rate of 8%), and the least stable were funds from individuals (bankruptcy rate of 28.5%). The relationship between financial stability and the amount of capital and the structure of bank loans is less pronounced. However, one can say that banks that focused on lending to individuals experienced a worse crisis than banks whose main borrowers were legal entities. The tools considered in the article (the Kohonen self-organizing neural networks and maps) allow for efficiently segmenting data samples according to various criteria, including bank solvency. The “hazardous” zones with a high bankruptcy rate (up to 49.2%) and the “safe” zone with a low rate of bankruptcy (6.3%) were highlighted on the map constructed. These results are of practical value and can be used in analyzing and selecting counterparties in the banking system during a downturn.

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Zenkouar, H., and A. Nachit. "Image coding using wavelet transforms and vector quantization with error correction." Robotica 17, no. 2 (March 1999): 219–27. http://dx.doi.org/10.1017/s026357479900082x.

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Image compression is essential for applications such as transmission of databases, etc. In this paper, we propose a new scheme for image compression combining recursive wavelet transforms with vector quantization. This method is based on the Kohonen Self-Organizing Maps (SOM) which take into account features of a visual system in both space and frequency domains.

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Ganhadeiro, Thiago, Eliane Christo, Lidia Meza, Kelly Costa, and Danilo Souza. "Evaluation of Energy Distribution Using Network Data Envelopment Analysis and Kohonen Self Organizing Maps." Energies 11, no. 10 (October 9, 2018): 2677. http://dx.doi.org/10.3390/en11102677.

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This article presents an alternative way of evaluating the efficiency of the electric distribution companies in Brazil. This assessment is currently performed and designed by the National Electric Energy Agency (ANEEL), a Brazilian regulatory agency, to regulate energy prices. This involves calculating the X-factor, which represents the efficiency evolution in the price-cap regulation model. The proposed model aims to use a network Data Envelopment Analysis (DEA) model with the network dimension as an intermediate variable and to use Kohonen Self-Organizing Maps (SOM) to correct the difficulties presented by environmental variables. In order to find which environmental variables influence the efficiency, factor analysis was used to reduce the dimensionality of the model. The analysis still uses multiple regression with the previous efficiency as the dependent variable and the four factors extracted from factor analysis as independent variables. The SOM generated four clusters based on the environment and the efficiency for each distributor in each group. This allows for a better evaluation of the correction in the X-factor, since it can be conducted inside each cluster with a maintained margin for comparison. It is expected that the use of this model will reduce the margin of questioning by distributors about the evaluation.

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Shklovets, A. V., and N. G. Axak. "Visualization of high-dimensional data using two-dimensional self-organizing piecewise-smooth Kohonen maps." Optical Memory and Neural Networks 21, no. 4 (October 2012): 227–32. http://dx.doi.org/10.3103/s1060992x12040066.

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Boszke, Patrycja, and Aleksander Maria Astel. "Self-organizing maps of Kohonen as means of Chara and Nitella species distribution diagnostic." Phycologia 49, no. 6 (November 2010): 592–603. http://dx.doi.org/10.2216/09-72.1.

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Tynchenko, V. S., V. V. Tynchenko, V. V. Bukhtoyarov, V. V. Kukartsev, V. A. Kukartsev, and D. V. Eremeev. "Application of Kohonen self-organizing maps to the analysis of enterprises’ employees certification results." IOP Conference Series: Materials Science and Engineering 537 (June 18, 2019): 042010. http://dx.doi.org/10.1088/1757-899x/537/4/042010.

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Ersoy, Orkun, Erkan Aydar, Alain Gourgaud, Harun Artuner, and Hasan Bayhan. "Clustering of volcanic ash arising from different fragmentation mechanisms using Kohonen self-organizing maps." Computers & Geosciences 33, no. 6 (June 2007): 821–28. http://dx.doi.org/10.1016/j.cageo.2006.10.008.

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Cooper, Cameron, and Andrew Burns. "Kohonen Self-organizing Feature Maps as a Means to Benchmark College and University Websites." Journal of Science Education and Technology 16, no. 3 (May 15, 2007): 203–11. http://dx.doi.org/10.1007/s10956-007-9053-7.

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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 (December 20, 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 principles from biological brains.6) The papers in this issue are organized as follows: The first two papers present material on organization and adaptation in the human brain. The third paper, by Kasabov, presents a novel approach to building open structured adaptive systems for on-line adaptation called evolving connectionist systems. The fourth paper by Kawahara and Saito suggests a method for building virtually connected adaptive cell structures. Papers 5 and 6 discuss the use of genetic algorithms and evolutionary computation for optimizing and adapting the structure of an intelligent system. The last two papers suggest methods for adaptive learning of a sequence of data in a feed-forward neural network that has a fixed structure. References: 1) D.O. Hebb, "The Organization of Behavior," Jwiley, New York, (1949). 2) T. Kohonen, "Self-organisation and associative memory," Springer-Verlag, Berlin, (1988). 3) T. Kohonen, "Self-Organizing Maps, second edition," Springer Verlag, (1997). 4) G. Carpenter and S. Grossberg, "Pattern recognition by self-organizing neural networks," The MIT Press, Cambridge, Massachusetts, (1991). 5) N. Kasabov, "Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering," The MIT Press, CA, MA, (1996). 6) S. Amari and N. Kasabov "Brain-like Computing and Intelligent Information Systems," Springer Verlag, Singapore, (1997).

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de Matos, Marcílio Castro, Paulo Léo Osorio, and Paulo Roberto Johann. "Unsupervised seismic facies analysis using wavelet transform and self-organizing maps." GEOPHYSICS 72, no. 1 (January 2007): P9—P21. http://dx.doi.org/10.1190/1.2392789.

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Unsupervised seismic facies analysis provides an effective way to estimate reservoir properties by combining different seismic attributes through pattern recognition algorithms. However, without consistent geological information, parameters such as the number of facies and even the input seismic attributes are usually chosen in an empirical way. In this context, we propose two new semiautomatic alternative methods. In the first one, we use the clustering of the Kohonen self-organizing maps (SOMs) as a new way to build seismic facies maps and to estimate the number of seismic facies. In the second method, we use wavelet transforms to identify seismic trace singularities in each geologically oriented segment, and then we build the seismic facies map using the clustering of the SOM. We tested both methods using synthetic and real seismic data from the Namorado deepwater giant oilfield in Campos Basin, offshore Brazil. The results confirm that we can estimate the appropriate number of seismic facies through the clustering of the SOM. We also showed that we can improve the seismic facies analysis by using trace singularities detected by the wavelet transform technique. This workflow presents the advantage of being less sensitive to horizon interpretation errors, thus resulting in an improved seismic facies analysis.

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Kuravsky, L. S., G. A. Yuryev, P. V. Scribtsov, M. A. Chervonenkis, A. A. Konstantinovsky, A. A. Shevchenko, and S. S. Isakov. "Quantitative criteria for recognizing the incorrect behavior of computer network users." Experimental Psychology (Russia) 11, no. 3 (2018): 19–35. http://dx.doi.org/10.17759/exppsy.2018110302.

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Two approaches for recognizing the incorrect behavior of computer network users are presented. The first one relies on the technique of statistical hypotheses testing and uses self-organizing feature maps (Kohonen networks) for generating target statistics. The second approach recognizes dangerous activity using executed sequences of relevant typical actions, with their dynamics being represented with the aid of Markov chains.

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Burton, Robert M., and David C. Plaehn. "One-dimensional Kohonen maps are super-stable with exponential rate." Advances in Applied Probability 31, no. 02 (June 1999): 367–93. http://dx.doi.org/10.1017/s0001867800009162.

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Kohonen self-organizing interval maps are considered. In this model a linear graph is embedded randomly into the unit interval. At each time a point is chosen randomly according to a fixed distribution. The nearest vertex and some of its nearby neighbors are moved closer to the point. These models have been proposed as models of learning in the audio-cortex. The models possess not only the structure of a Markov chain, but also the added structure of a random dynamical system. This structure is used to show that for a large class of these models, in a strong way, the initial conditions are unimportant and only the dynamics govern the future. A contractive condition is proven in spite of the fact that the maps are not continuous. This, in turn, shows that the Markov chain is uniformly ergodic.

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Burton, Robert M., and David C. Plaehn. "One-dimensional Kohonen maps are super-stable with exponential rate." Advances in Applied Probability 31, no. 2 (June 1999): 367–93. http://dx.doi.org/10.1239/aap/1029955140.

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Kohonen self-organizing interval maps are considered. In this model a linear graph is embedded randomly into the unit interval. At each time a point is chosen randomly according to a fixed distribution. The nearest vertex and some of its nearby neighbors are moved closer to the point. These models have been proposed as models of learning in the audio-cortex. The models possess not only the structure of a Markov chain, but also the added structure of a random dynamical system. This structure is used to show that for a large class of these models, in a strong way, the initial conditions are unimportant and only the dynamics govern the future. A contractive condition is proven in spite of the fact that the maps are not continuous. This, in turn, shows that the Markov chain is uniformly ergodic.

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Schulz, Reiner, and James A. Reggia. "Temporally Asymmetric Learning Supports Sequence Processing in Multi-Winner Self-Organizing Maps." Neural Computation 16, no. 3 (March 1, 2004): 535–61. http://dx.doi.org/10.1162/089976604772744901.

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We examine the extent to which modified Kohonen self-organizing maps (SOMs) can learn unique representations of temporal sequences while still supporting map formation. Two biologically inspired extensions are made to traditional SOMs: selection of multiple simultaneous rather than single “winners” and the use of local intramap connections that are trained according to a temporally asymmetric Hebbian learning rule. The extended SOM is then trained with variable-length temporal sequences that are composed of phoneme feature vectors, with each sequence corresponding to the phonetic transcription of a noun. The model transforms each input sequence into a spatial representation (final activation pattern on the map). Training improves this transformation by, for example, increasing the uniqueness of the spatial representations of distinct sequences, while still retaining map formation based on input patterns. The closeness of the spatial representations of two sequences is found to correlate significantly with the sequences' similarity. The extended model presented here raises the possibility that SOMs may ultimately prove useful as visualization tools for temporal sequences and as preprocessors for sequence pattern recognition systems.

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Holubar, P., L. Zani, M. Hagar, W. Fröschl, Z. Radak, and R. Braun. "Modelling of anaerobic digestion using self-organizing maps and artificial neural networks." Water Science and Technology 41, no. 12 (June 1, 2000): 149–56. http://dx.doi.org/10.2166/wst.2000.0259.

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In this work the training of a self-organizing map and a feed-forward back-propagation neural network was made. The aim was to model the anaerobic digestion process. To produce data for the training of the neural nets an anaerobic digester was operated at steady state and disturbed by pulsing the organic loading rate. Measured parameters were: gas composition, gas production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and chemical oxygen demand of feed and effluent. It could be shown that both types of self-learning networks in principle could be used to model the process of anaerobic digestion. Using the unsupervised Kohonen self-organizing map, the model's predictions could not follow the measurements in all details. This resulted in an unsatisfactory regression coefficient of R2= 0.69 for the gas composition and R2= 0.76 for the gas production rate. When the supervised FFBP neural net was used the training resulted in more precise predictions. The regression coefficient was found to be R2= 0.74 for the gas composition and R2== 0.92 for the gas production rate.

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Ronald, Ronald, and Amelia Amelia. "Clustering Analysis of Students’ Culture and Behavior for University Choice Using Kohonen Self Organizing Map." Indian Journal of Applied Research 4, no. 3 (October 1, 2011): 270–73. http://dx.doi.org/10.15373/2249555x/mar2014/83.

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Kolasa, Marta, Rafał Długosz, Wojciech Jóźwicki, Jolanta Pauk, Aleksandra Świetlicka, and Pierre André Farine. "Analysis of Significant Prognostic Factors of Patients with Bladder Cancer Using Self-Organizing Maps." Solid State Phenomena 199 (March 2013): 223–28. http://dx.doi.org/10.4028/www.scientific.net/ssp.199.223.

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This study presents a new approach to determine significant prognostic factors for patients suffering from the bladder cancer. The analysis of medical data has been performed by the use of the Kohonen self-organizing map (SOM). The SOM allows visualizing and identifying the prognostic factors indicating which of them are significant. A database comprised of ninety patients has been used in this study. Seven predictors were investigated. The cluster analysis indicates that the significant prognostic factors for the bladder cancer are: histological grade (cG) and stage (cT). The obtained results also showed that the sex and the cG variables are highly correlated and that the number of non-classic differentiation (NDNc) features in bladder cancer is somewhat correlated to surgically removed lymphnode number (LN) and metastatic positive lymphnode number (PLN).

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Journal articles on the topic 'Self-organizing maps of Kohonen'

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