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Bibliography of Self-Organizing Map (SOM) Papers: 1998-2001 Addendum

Profile image of Jari KangasJari Kangas

2003, Neural Computing Surveys

Abstract

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied ...

Key takeaways
sparkles

AI

  1. The bibliography contains 2092 new articles on Self-Organizing Maps (SOM) published from 1998 to 2001.
  2. A total of 3343 papers were published on SOM from 1981 to 1997, with updates for newer research.
  3. Thirteen topical categories were created to analyze the evolution of SOM research applications.
  4. A WEBSOM interface maps 2426 documents to visualize thematic relationships within the collection.
  5. The paper includes keyword and thematic indices to assist users in navigating the extensive bibliography.
Figures (5)
Figure 1: Evolution of SOM research. Each horizontal bar shows the number of published SOM works within a certain topic category, as a function of the publication year shown in the bottom. The total number of works in the category is shown in parentheses. The categories are scaled individually. White: largest number within the category, black: zero articles.
Figure 1: Evolution of SOM research. Each horizontal bar shows the number of published SOM works within a certain topic category, as a function of the publication year shown in the bottom. The total number of works in the category is shown in parentheses. The categories are scaled individually. White: largest number within the category, black: zero articles.
Information science and docu- mentation (83)
Information science and docu- mentation (83)
Figure 3: Search example: The user has searched for documents about WEBSOM. The circles in the map indicate best matches. A closer view is obtained by clicking an interesting area. Clicking the zoomed map reveals the articles at the map nodes.
Figure 3: Search example: The user has searched for documents about WEBSOM. The circles in the map indicate best matches. A closer view is obtained by clicking an interesting area. Clicking the zoomed map reveals the articles at the map nodes.

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An Intuitive Guide of Self Organizing Maps with Practical Implementation in Minisom
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The Self Organizing Map is one of the most popular neural models. It belongs to the category of the competitive learning network. The SOM is based on unsupervised learning, which means that is no human intervention is needed during the training and those little needs to be known about characterized by the input data. We could, for example, use the SOM for clustering membership of the input data. The SOM can be used to detect features inherent to the problem and thus has also been called SOFM the Self Origination Feature Map.

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SOM PAK: The self-organizing map program package
Jari Kangas

Report A31, Helsinki …, 1996

Abstract: The Self-Organizing Map (SOM) represents the result of a vector quantization algorithm that places a number of reference or code-book vectors into a high-dimensional input data space to approximate to its data sets in an ordered fashion. The SOM PAK program package ...

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Robust adaptive learning approach to self-organizing maps
Bekir Karlik

Knowledge-Based Systems, 2019

The Self-Organizing Map (SOM) has attracted much research in diverse fields of study, where it has been successfully applied to a wide range of artificial intelligence applications. In this paper, a new intelligent adaptive learning SOM, in contrast with the conventional SOM algorithm, is presented. The proposed SOM overcomes the disadvantages of the conventional SOM by deriving a new variable learning rate that can adaptively achieve the optimal weights and obtain the winner neurons with a shorter learning process time. To quantify the performance, the proposed SOM was compared with various unsupervised algorithms to examine how well the network weight update equation adapts and reaches the optimum weights with fewer iterations. Extensive experiments were conducted using eight different databases from the UCI and KEEL repositories. The proposed SOM algorithm was further compared with the conventional SOM, GF-SOM, PLSOM, and PLSOM2 algorithms. The proposed SOM algorithm showed superiority in terms of the convergence rate, Quantization Error (QE), Topology Error (TE) preserving map, and accuracy during the classification process.

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Applications of Self-Organizing Maps
Magnus Johnsson

2012

The self-organizing map is an unsupervised neural network first described by the Finnish scientist Teuvo Kohonen. By a self-organizing adaptation process this neural network learns to map the input space onto a discretized representation which preserves the topology and reflects the probability distribution of the input. This book is about how the original selforganizing map as well as variants and extensions of it can be applied in different fields. In fourteen chapters, a wide range of applications of the Self-Organizing Map is discussed. To name a few, these applications include the analysis of financial stability, the fault diagnosis of plants, the creation of well-composed heterogeneous teams and the application of the self-organizing map to the atmospheric sciences.

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Self-organizing maps with information theoretic learning
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Neurocomputing, 2015

The self organizing map (SOM) is one of the popular clustering and data visualization algorithms and has evolved as a useful tool in pattern recognition, data mining since it was first introduced by Kohonen. However, it is observed that the magnification factor for such mappings deviates from the information-theoretically optimal value of 1 (for the SOM it is 2/3). This

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The Self Organizing Map as a Tool for Cluster Analysis
Abdu Masanawa Sagir

Menemui Matematik (Discovering Mathematics), 2016

The Self-organizing map is among the most acceptable algorithm in the unsupervised learning technique for cluster analysis. It is an important tool used to map high-dimensional data sets onto a low-dimensional discrete lattice of neurons. This feature is used for clustering and classifying data. Clustering is the process of grouping data elements into classes or clusters so that items in each class or cluster are as similar to each other as possible. In this paper, we present an overview of self organizing map, its architecture, applications and its training algorithm. Computer simulations have been analyzed based on samples of data for clustering problems.

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A Statistical Tool to Assess the Reliability of Self-Organizing Maps
Marie Cottrell

Advances in Self-Organising Maps, 2001

Making results reliable is one of the major concerns in artificial neural networks research. It is often argued that Self-Organizing Maps are less sensitive than other neural paradigms to problems related to convergence, local minima, etc. This paper introduces objective statistical measures that can be used to assess the stability of the results of SOM, both on the distortion and on the topology preservation points of views.

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A Comparative Performance Analysis of Self Organizing Maps on Weight Initializations Using different Strategies
Prema Nedungadi

2015 Fifth International Conference on Advances in Computing and Communications (ICACC), 2015

Self Organizing Maps perform clustering of data based on unsupervised learning. It is of concern that initialization of the weight vector contributes significantly to the performance of SOM and since real world datasets being high-dimensional, the complexity of SOM tend to increase tremendously leading to increased time consumption as well. Our work focuses on the analysis of different weight initialization strategies and various dimensionality reduction measures with the intent to make SOM flexible for handling high-dimensional datasets. We use two methods of comparison, one on projected space and another before projection. The datasets used are real world datasets taken from UCI repository.

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Clustering of the Self-Organizing Map
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Abstract—The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using -means are investigated. The two-stage procedure—first using SOM to produce the prototypes that are then clustered in the second stage—is found to perform well when compared with direct clustering of the data and to reduce the computation time.

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Variants of self-organizing maps
Jorma Laaksonen

IEEE Transactions on Neural Networks, 1990

Abstmct-The self-organizing maps have a bearing on traditional vector quantization. A characteristic that makes them more resemble certain biological brain maps, however, is the spatial order of their responses, which is formed in the learning process. This paper discusses the basic algorithms and two innovations: dynamic weighting of the input signals at each input of each cell, which improves the ordering when very different input signals are used, and definition of neighborhoods in the learning algorithm by the minimal spanning tree, which provides a far better and faster approximation of prominently structured density functions. Finally, it is cautioned that if the maps are used for pattern recognition and decision processes, it is necessary to fine tune the reference vectors so that they directly define the decision borders.

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References (2,113)

  1. Timo Honkela, Samuel Kaski, Krista Lagus, and Teuvo Kohonen. Newsgroup exploration with WEBSOM method and browsing interface. Technical Report A32, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland, 1996.
  2. Samuel Kaski, Jari Kangas, and Teuvo Kohonen. Bibliography of self-organizing map (SOM) pa- pers: 1981-1997. Neural Computing Surveys, 1(3&4):1-176, 1998. Available in electronic form at http://www.icsi.berkeley.edu/ £ jagota/NCS/: Vol 1, pp. 102-350.
  3. Teuvo Kohonen. Automatic formation of topological maps of patterns in a self-organizing system. In Erkki Oja and Olli Simula, editors, Proc. 2SCIA, Scand. Conf. on Image Analysis, pages 214-220, Helsinki, Finland, 1981. Suomen Hahmontunnistustutkimuksen Seura r. y.
  4. Teuvo Kohonen. Self-organizing formation of topologically correct feature maps. Biol. Cyb., 43(1):59-69, 1982.
  5. Teuvo Kohonen. Self-Organizing Maps. Springer, Berlin, Heidelberg, 1995. (Third Extended Edition 2001). 947, 954, 951, 956, 1009, 1017, 1046, 1049, 1075, 1068, 1105, 1110, 1164, 1305, 1345, 1356, 1434, 1542, 1645, 1684, 1818, 1903, 1910, 1381, 1923, 1954, 1953, 1974, 2021, 2060] bankruptcy [25, 218, 2086, 802, 800, 801, 790, 856, 857] Bayesian [441, 446, 445, 833, 750, 860, 1026, 1068, 1120, 1138, 1211, 1210, 1252, 1263, 1264, 1625, 1684, 1729, 1742, 1800, 1817, 1829, 1831, 1832, 1955, 1956, 2007, 2039] benchmark [17, 172, 190, 189, 191, 213, 323, 354, 384, 484, 981, 1190, 1318, 1988, 1319, 1847, 1020, 1982] BGA [1499, 2051] binding problem [937] binocular [103, 1963] biochemical [868, 1163] biological [15, 31, 136, 158, 194, 207, 260, 263, 268, 285, 376, 428, 453, 480, 479, 689, 741, 887, 923, 948, 1017, 1057, 1139, 1146, 1148, 1163, 1164, 1235, 1219, 1372, 1371, 1370, 1392, 1414, 1426, 1511, 1513, 1519, 1560, 1668, 1736, 1861, 1912, 1913, 1923, 1928, 1950, 2029] biological macromolecules [1163, 1372, 1371, 1370] biomedical [35, 393, 394, 1911] Boltzmann machine [1274] brain [9, 54, 74, 126, 133, 149, 231, 243, 268, 359, 420, 423, 422, 470, 527, 531, 537, 623, 781, 783, 887, 893, 1044, 1059, 1149, 1230, 1290, 1384, 1392, 1421, 1420, 1570, 1762, 2088, 1969, 1970, 1971] brain image [1044] brain voxels [422] brainstem [393, 394, 1011] breast cancer [223, 367, 1542, 1599, 1684, 1962] bus [239, 246, 247, 734, 904, 1007, 1092, 1091, 2028] c-means [124, 258, 556, 775, 778, 780, 822, 939, 938, 1017, 1044, 1347, 1373, 1372, 1371, 1370, 1578, 1638, 1763, 1792, 1930] CAD [30, 84, 162, 939] cancer [90, 223, 228, 241, 261, 367, 384, 395, 820, 1086, 1139, 1141, 1140, 1542, 1599, 1684, 1962] cardiology [352, 448, 1469, 1606, 1756, 1755] CDMA [498, 657, 653, 654, 656, 655] cepstrum [254, 305, 2056] cerebral cortex [231, 948] chaos [329, 530, 1875] character recognition [117, 167, 235, 326, 610, 766, 946, 965, 987, 986, 1062, 1063, 1197, 1348, 1406, 1450, 1549, 1804, 1381, 2008, 2013, 2047] characters [167, 326, 610, 766, 946, 1905, 1906, 1908, 1993] chemical analysis [1242, 1243, 1312, 1313, 1775, 1785, 1777, 1780, 1784, 1944, 1945, 1946] chemical databases [119, 1502] chemical spectra [1243, 1309, 1310] chinese character recognition [235, 946, 1062, 1450, 1804] chip [382, 419, 486, 926, 1006, 1089, 1087, 1088, 1499, 1527, 1528, 1606, 1605] chromosomes [381, 380, 552, 961, 960, 1141, 1291] circuit [358, 406, 405, 486, 488, 580, 617, 734, 840, 926, 996, 1024, 1088, 1392, 1499, 1509, 1516, 1554, 1605, 1611, 1653, 1759, 1776, 1967, 2028, 2043] climate [192, 395, 1113] clinical [126, 207, 243, 550, 588, 725, 949, 1141, 1140, 1392, 1566, 1604, 1732, 1755, 1812, 1854, 1890, 1942, 1962, 1943, 2056] cloud classification [34, 2086, 962, 1764, 1765] cloud images [255, 962] CMOS [926, 996, 1089, 1087, 1088, 1418, 1509, 1605] central nervous system (CNS) [1399, 1538] co-occurrence [51, 2086, 387, 604, 696, 860, 1046, 1133, 1407, 1744, 1764, 1837, 1854] coarticulation [1806] cognitive [423, 477, 580, 1392, 1907, 1950, 1980] coherence [134, 168, 1194, 1269, 1345, 1744] color [50, 55, 56, 168, 211, 229, 277, 275, 352, 446, 445, 491, 540, 546, 585, 604, 626, 638, 674, 675, 835, 760, 804, 795, 809, 808, 820, 845, 840, 848, 862, 897, 922, 935, 936, 937, 966, 971, 1017, 1294, 1031, 1077, 1078, 1109, 1133, 1027, 1317, 1316, 1324, 1329, 1343, 1344, 1347, 1361, 1360, 1358, 1379, 1386, 1390, 2092, 1472, 1543, 1671, 1435, 1763, 1774, 1792, 1825, 1831, 1876, 1920, 1994, 2041] color segmentation [1017, 2041] color-based segmentation [2041] coloring [307, 626, 804, 795, 803, 897, 2037] complexity [4, 5, 47, 57, 71, 82, 127, 278, 636, 437, 506, 628, 719, 720, 836, 920, 974, 989, 1047, 1131, 1153, 1263, 1330, 1331, 1344, 1378, 1390, 1389, 1426, 1493, 1502, 1577, 1742, 1744, 1771, 1919, 1918, 1962, 2034, 2037, 2063] compounds [15, 57, 173, 361, 360, 426, 538, 629, 903, 928, 1122, 1366, 1399, 1502, 1754, 2018] compressed video [910, 909] compressing images [1162] compression [2, 60, 61, 64, 67, 80, 81, 82, 146, 150, 172, 193, 210, 2086, 283, 281, 323, 540, 684, 752, 784, 782, 1028, 1029, 1109, 1162, 1338, 1363, 1364, 1379, 1479, 1587, 1592, 1607, 1657, 1662, 1661, 1683, 1733, 1736, 1747, 1859, 2006, 2064, 2077] conjugate-gradient method [728] control [19, 52, 72, 102, 103, 111, 125, 130, 136, 152, 158, 165, 176, 233, 265, 266, 264, 309, 310, 351, 358, 364, 366, 372, 377, 396, 418, 424, 428, 437, 484, 494, 501, 502, 510, 535, 536, 551, 575, 584, 599, 606, 615, 253, 651, 657, 653, 654, 656, 655, 699, 708, 734, 743, 746, 760, 786, 805, 816, 851, 935, 936, 947, 982, 1000, 1005, 1024, 1054, 1059, 1110, 1111, 1149, 1220, 1239, 1212, 1224, 1226, 1217, 1236, 1223, 1233, 1218, 1240, 1216, 1246, 1247, 1256, 1254, 1255, 1266, 1271, 1318, 1329, 1365, 1374, 1394, 1422, 1424, 1426, 1448, 2092, 1484, 1509, 1319, 1538, 1576, 1605, 1612, 1614, 1679, 1683, 1722, 1755, 1793, 1805, 1807, 2087, 2088, 1833, 1834, 1835, 1876, 1886, 1879, 1910, 1914, 1940, 2052, 1968, 1987, 1994, 2003, 2004, 1996, 1997, 2008, 2026, 2050, 2055, 2074, 2075, 2081] control system [165, 265, 366, 377, 428, 494, 584, 1940, 2003, 2004, 1997] convergence [1, 80, 81, 93, 94, 104, 112, 115, 113, 202, 213, 233, 372, 381, 384, 433, 435, 453, 460, 576, 680, 691, 737, 827, 831, 846, 866, 914, 959, 998, 1051, 1052, 1199, 1252, 1250, 1281, 1328, 1524, 1529, 1532, 1551, 1553, 1639, 1656, 1745, 1808, 1857, 1855, 1860, 1858, 1930, 1973, 1982, 1987, 2040, 2062] coronary [271, 447, 1684] cortex [74, 100, 99, 231, 434, 444, 480, 481, 483, 478, 477, 482, 479, 496, 614, 751, 757, 948, 1009, 1021, 1146, 1147, 1172, 1173, 1180, 1181, 1196, 1233, 1235, 1219, 1240, 1241, 1384, 1397, 1396, 1398, 1421, 1538, 1619, 1624, 1649, 1647, 1648, 1724, 1723, 1725, 1726, 1752, 1762, 1907, 1914, 1950] cortical map(s) [481, 478, 479, 1021, 1172, 1173, 1196, 1233, 1235, 1219, 1240, 1241, 1397, 1396, 1726] counter-propagation [491, 1206, 1305] cross-validation [69, 173, 223, 1684, 1811] cursive character [167, 610, 1227] curvilinear component analysis (CCA) [530, 1013, 1875] data fusion [159, 589, 1017, 1061, 1545, 1603, 1746, 1750, 1917, 2065, 2066, 2068, 2069] data mining [17, 16, 18, 215, 304, 306, 307, 311, 330, 337, 385, 388, 399, 425, 467, 470, 510, 520, 557, 564, 805, 828, 829, 843, 842, 866, 882, 875, 877, 844, 976, 978, 998, 1006, 1033, 1030, 1032, 1129, 1158, 1170, 1242, 1243, 1309, 1312, 1310, 1314, 1311, 1308, 1438, 1451, 1530, 1577, 1034, 1610, 1620, 1716, 1757, 1777, 1794, 1822, 1849, 1862, 1869, 1870, 1884, 1885, 1932, 1989, 2015, 2016] data visualization [27, 284, 281, 470, 484, 622, 621, 626, 625, 682, 833, 801, 790, 855, 862, 863, 1184, 1768, 1863, 1862, 1861, 1872, 1867, 1885] data-analysis [469, 789] database [21, 30, 35, 50, 69, 71, 86, 95, 116, 113, 120, 119, 125, 167, 168, 207, 210, 215, 229, 248, 279, 304, 305, 311, 326, 357, 373, 399, 400, 425, 431, 443, 448, 472, 562, 607, 608, 638, 657, 678, 710, 711, 708, 709, 728, 769, 766, 821, 823, 862, 863, 890, 872, 927, 939, 944, 958, 966, 967, 970, 988, 1006, 1001, 1033, 1023, 1065, 1064, 1062, 1103, 1130, 1129, 1157, 1174, 1245, 1258, 1271, 1312, 1310, 1307, 1311, 1308, 1317, 1316, 1322, 1377, 1390, 1399, 2092, 1502, 1519, 1532, 1589, 1603, 1620, 1622, 1668, 1669, 1684, 1705, 1710, 1716, 1744, 1758, 1787, 1790, 1789, 1796, 1797, 2088, 1831, 1888, 1887, 1895, 1897, 1908, 1933, 1940, 1962, 2014, 2026] discrete cosine transform (DCT) [150, 1065, 1136, 1285, 1592, 1837] densitometric [359] density estimation [281, 479, 548, 681, 1828, 1840, 1847, 1960, 2040] diabetes [90, 1684] diagnostics [3, 38, 80, 81, 126, 223, 224, 227, 241, 254, 256, 313, 314, 352, 386, 391, 393, 394, 395, 405, 411, 415, 541, 547, 555, 590, 726, 732, 769, 774, 781, 839, 868, 911, 925, 927, 1929, 1117, 1139, 1335, 1375, 1419, 1422, 1518, 1542, 1594, 1607, 1643, 1684, 1717, 1732, 1735, 1739, 1755, 1757, 1799, 1811, 1853, 1854, 1911, 1912, 1913, 1955, 1961, 1962, 1979, 2021, 2020, 2045, 2056, 2075] digit recognition [248, 529, 833, 1063, 1383, 1382, 1550, 1700, 2057] digital images [35, 51, 359, 1640, 1758] digital libraries [6, 708, 792, 1169, 1456, 1454, 1459, 1460] dimensionality reduction [2, 265, 501, 515, 953, 955, 957, 1193, 2022, 1253, 1353, 1550, 2023] disease [90, 149, 254, 538, 868, 927, 1574, 1599, 1684, 1961] dynamic learning vector quantization (DLVQ) [2053] DNA sequences [372, 382, 530, 764, 1875] DNA microarray [228, 372, 383, 382, 1521, 1796, 1797] document [7, 6, 8, 67, 68, 200, 250, 353, 368, 369, 370, 495, 528, 529, 543, 561, 562, 563, 607, 608, 642, 643, 640, 710, 711, 708, 750, 754, 790, 793, 789, 821, 895, 886, 894, 890, 869, 872, 880, 896, 944, 945, 943, 976, 977, 975, 979, 1002, 1016, 1026, 1045, 1099, 1130, 1129, 1169, 1167, 1245, 1274, 1277, 1316, 1403, 1430, 1452, 1462, 1463, 1453, 1455, 1461, 1458, 1459, 1460, 1494, 1505, 1639, 1661, 1688, 1689, 1755, 1838, 1896, 1895, 1897, 2015, 2016, 2017, 2034] drug [41, 43, 385, 911, 1376, 1563, 1732] dynamic time warping (DTW) [277, 1906] dysphonia [166] economics [337, 338] edge detection [40, 1067, 1189, 1750, 1773, 1774] EEG (electroencephalogram) [75, 539, 559, 723, 722, 724, 725, 751, 949, 1059, 1149, 1205, 1391, 1398, 1419, 1421, 1420, 1854] electric load [185, 186, 184, 183, 615, 834, 1380, 2087] electric power [154, 834, 1000, 1367, 1468, 1467, 1485, 1591] electrical [207, 386, 393, 615, 732, 1013, 1366, 1576, 1679, 1757, 1950, 2060] electrocardiogram (ECG) [83, 142, 147, 448, 447, 1049, 1157, 1340, 1582, 1720, 2064] electrode [1059, 1421] electromagnetic [118, 397, 413, 515, 758, 1520, 1717, 1814, 1816, 1815] electromyogram (EMG) [3, 254, 256, 253, 1812, 2052] electron spectroscopy [1242, 1243, 1309, 1780] electron-microscopy images [1163, 1164] electronics [1793] EM algorithm [63, 116, 478, 479, 553, 1026, 1193, 2022, 1219, 1767, 2023] endocardial [1470] engine [95, 277, 374, 541, 590, 1001, 1038, 1987, 2049, 2075] English [95, 768, 1002, 1033, 1055, 1729, 1897] entropy [69, 212, 444, 684, 750, 784, 778, 780, 1069, 1162, 1281, 1357, 1377, 1070, 1707, 1847, 1862, 1927, 2005, 2040, 2067] entropy maximization [1707, 2005] environmental [194, 209, 494, 523, 756, 805, 929, 1279, 1287, 1304, 1384, 1629, 1764, 1765, 1873, 1912, 1913, 2052] epileptic [723, 724, 725, 1854, 1970] equalization [1, 144, 241, 437, 691, 1124, 1362] evoked potentials [393, 394] evolutionary [14, 74, 189, 191, 214, 345, 591, 637, 657, 851, 879, 1022, 1263, 1299, 1297, 1298, 1318, 1355, 1422, 1500, 1319, 1814, 1883, 1890, 1889, 1900, 1922, 2076, 1981, 1987, 2059] exploration [49, 54, 215, 228, 242, 268, 303, 316, 389, 394, 395, 470, 526, 527, 530, 557, 564, 576, 581, 603, 642, 743, 796, 791, 800, 790, 807, 815, 813, 814, 812, 855, 857, 862, 863, 869, 872, 878, 920, 944, 976, 990, 1002, 1041, 1057, 1107, 1148, 1170, 1169, 1219, 1246, 1264, 1267, 1270, 1304, 1307, 1308, 1323, 1371, 1409, 1443, 1453, 1455, 1461, 1578, 1632, 1656, 1713, 1786, 1797, 1801, 1861, 1869, 1867, 1875, 1893, 1919, 1917, 1927, 1918, 2052, 1980, 2011] face image [122, 647, 669, 806, 824, 1193, 1702, 1701, 1789] fault diagnosis [38, 313, 405, 726, 732, 839, 1757, 1811, 1979, 2045] feedback [103, 351, 377, 502, 572, 652, 649, 648, 750, 868, 966, 967, 971, 1049, 1258, 1271, 1355, 1530, 1560, 1811, 1934, 1967, 1935] feedforward [77, 130, 208, 218, 240, 270, 310, 353, 374, 396, 419, 508, 517, 655, 707, 780, 949, 1049, 1111, 1120, 1138, 1139, 1164, 1330, 1331, 1533, 1538, 1563, 1566, 1771, 1814, 2053, 2083, 2085] fermentation [401] FFT [438, 524, 751, 957, 1064, 1148, 1398, 1419, 1799] fiber-optic [1246] filtering [40, 145, 200, 305, 309, 324, 353, 387, 421, 446, 445, 501, 502, 514, 560, 576, 575, 611, 253, 638, 667, 688, 691, 693, 727, 750, 751, 752, 923, 978, 1049, 1127, 1247, 1329, 1407, 1408, 1431, 1507, 1514, 1515, 1513, 1601, 1614, 1638, 1750, 1793, 1811, 1814, 1840, 1847, 1914, 1937, 2047, 2066, 2068, 2069, 2077, 2081] finance [6, 25, 49, 70, 286, 342, 329, 332, 341, 331, 337, 339, 338, 402, 525, 788, 800, 843, 855, 859, 844, 1251, 1473, 1474, 1594, 1595, 1596, 1597, 1682, 1745, 1779, 2088, 1873, 1958, 1990] fingerprint recognition [1933] finite-state vector quantization [2078] Fisher's linear discriminant (FLD) [3, 1193, 1684, 1804] fluorescence [173, 381, 380, 395, 592, 1574, 1796, 1817] FMRI [420, 423, 422, 477, 482, 1290, 1970] forecasting [185, 186, 184, 183, 226, 238, 255, 296, 318, 525, 547, 615, 834, 901, 902, 962, 1013, 1014, 1053, 1056, 1068, 1199, 1245, 1374, 1380, 1475, 1474, 1483, 1591, 1598, 1679, 1687, 1745, 2087, 1973, 2050] forward-looking infrared (FLIR) imagery [187, 1023, 1047, 1124, 1125, 1492, 1489] forest [204, 527, 596, 613, 1168, 1632, 1873, 1978]
  6. Fourier [242, 255, 406, 405, 414, 421, 524, 541, 550, 683, 707, 845, 957, 1115, 1149, 1341, 1375, 1744, 1799, 1854, 2056] fractal image [150, 530, 1136, 1875] fraud [634, 633, 1958] fuzzy [10, 18, 32, 57, 62, 63, 77, 79, 78, 124, 130, 134, 142, 152, 188, 208, 233, 237, 243, 258, 259, 262, 278, 303, 304, 310, 325, 329, 352, 364, 389, 388, 426, 494, 502, 509, 516, 556, 561, 606, 608, 627, 650, 663, 668, 672, 677, 676, 673, 684, 691, 725, 735, 737, 739, 741, 781, 784, 775, 782, 778, 783, 780, 821, 822, 841, 852, 853, 901, 910, 911, 929, 930, 931, 939, 938, 952, 951, 956, 1003, 1025, 1294, 1046, 1054, 1050, 1044, 1056, 1074, 1061, 1077, 1089, 1087, 1088, 1100, 1107, 1192, 1281, 1280, 1279, 1027, 1305, 1340, 1339, 1347, 1349, 1350, 1373, 1372, 1371, 1370, 1393, 1399, 1422, 1436, 1465, 1478, 1502, 1506, 1507, 1518, 1536, 1537, 1591, 1612, 1615, 1620, 1638, 1659, 1660, 1670, 1676, 1677, 1679, 1690, 1704, 1700, 1705, 1716, 1749, 1763, 1792, 1793, 1811, 1820, 1821, 2087, 1863, 1866, 1914, 1930, 1986, 1920, 1381, 1936, 1937, 1942, 1941, 1951, 1960, 1959, 1970, 1978, 1987, 1996, 1943, 2021, 2044, 2055, 2068, 2067, 2063, 2070, 2080] fuzzy artmap [426, 509, 627, 737, 1077, 1305, 1615, 1716, 1811] fuzzy c-means [258, 556, 775, 778, 780, 822, 939, 938, 1044, 1347, 1373, 1372, 1371, 1370, 1638, 1763, 1792, 1930] fuzzy clustering [78, 237, 259, 556, 561, 684, 784, 778, 780, 1294, 1027, 1340, 1399, 1502, 1506, 1507, 1863, 1936] fuzzy control [364, 494, 1054, 1914] Gabor filters [187, 612, 688, 923, 1407, 1408, 1513, 1847, 1914, 2047, 2077]
  7. Gaussian Markov random fields (GMRF) [1407] galaxy [278, 1108, 1107] gene expression [228, 791, 797, 790, 1323, 1478, 1521, 1737, 1796, 1797, 1798, 1830] generalized LVQ [235, 775, 1062, 1551, 1548] generative topographic mapping (GTM) [90, 129, 149, 213, 528, 529, 575, 754, 1104, 1768, 1767, 1829, 1863, 1862] genetic [99, 111, 124, 190, 191, 329, 403, 404, 429, 530, 552, 570, 691, 749, 764, 832, 851, 853, 879, 1022, 1068, 1135, 1141, 1163, 1164, 1199, 1263, 1304, 1355, 1383, 1409, 1413, 1411, 1419, 1466, 1474, 1500, 1591, 1699, 1693, 1751, 1797, 1814, 1875, 1920, 1973, 2080] geological [603, 860, 1148] geophysics [1138] geopotential [192, 1039] gesture recognition [277, 274, 276, 275, 408, 987, 986, 1119, 1246, 1302, 2092, 1547, 1810] grammar [572, 946] granulometric [1744] growing [19, 17, 16, 20, 18, 78, 83, 84, 117, 210, 216, 234, 352, 359, 368, 371, 369, 370, 376, 375, 413, 415, 484, 509, 588, 596, 665, 671, 670, 678, 682, 719, 720, 743, 746, 848, 910, 923, 1017, 1170, 1168, 1182, 1184, 1183, 1253, 1269, 1289, 1320, 1988, 1455, 1458, 1514, 1513, 1521, 1577, 1585, 1588, 1615, 1668, 1717, 1803, 1861, 1865, 1876, 1881, 1878, 1888, 1887, 1911, 1923, 1980, 1981, 2041] GSM [12, 437] GSOM [19, 20, 18, 31, 665, 934, 1888, 1887, 1892] handwriting [117, 235, 248, 326, 345, 346, 347, 373, 529, 554, 833, 754, 819, 946, 1042, 1074, 1062, 1075, 1063, 1227, 1221, 1348, 1383, 1382, 1550, 1700, 1804, 1905, 1906, 1908, 1381, 1958, 2008, 2044, 2057] hardware [135, 137, 146, 419, 691, 697, 698, 835, 904, 905, 1078, 1336, 1345, 1384, 1418, 1509, 1586, 1722, 1851, 1874] health [25, 38, 90, 174, 242, 243, 304, 313, 506, 507, 523, 901, 902, 1022, 1110, 1419, 1564, 1565, 1740, 1912, 1966, 2024, 2046] heart murmurs [1019, 2046]
  8. Hebbian [138, 480, 479, 572, 614, 687, 686, 702, 948, 1196, 1219, 1218, 1240, 1384, 1397, 1396, 1540, 1600, 1626, 1640, 1647, 1838, 2058] hidden Markov models (HMM) [145, 149, 277, 274, 276, 275, 345, 346, 347, 433, 432, 941, 942, 1026, 1278, 2092, 1497, 1575, 1590, 2089, 1965] hierarchical [19, 34, 82, 95, 105, 125, 126, 172, 185, 182, 186, 184, 179, 181, 183, 221, 220, 283, 284, 281, 323, 368, 371, 369, 370, 380, 416, 512, 702, 842, 866, 911, 937, 946, 966, 968, 969, 1001, 1090, 1122, 1170, 1175, 1202, 1249, 1329, 1384, 1390, 1389, 1097, 1452, 1480, 1528, 1555, 1560, 1572, 1612, 1680, 1690, 1710, 1708, 1709, 1734, 1767, 1809, 1837, 1869, 1896, 1897, 1908, 1938, 1961, 1970, 1994, 2015, 2016, 2054] hierarchical clustering [19, 34, 105, 323, 369, 380, 1175, 1097, 1970, 1994] high-energy physics [1427] histogram [55, 229, 241, 345, 346, 347, 645, 646, 644, 696, 695, 872, 1078, 1133, 1134, 1408, 1668, 1671, 1837, 1933] holographic [471, 472, 473, 1345] Hopfield network [216, 656, 1105, 1220, 1239, 1212, 1238, 1673, 2035] Hough transform [187, 1187, 1348, 2010] html [710, 711, 709, 976, 1105] hybrid [2, 51, 57, 88, 134, 135, 156, 165, 175, 226, 241, 277, 274, 276, 275, 278, 311, 345, 394, 428, 441, 494, 432, 531, 591, 609, 610, 629, 657, 656, 655, 725, 843, 901, 909, 954, 951, 844, 1002, 1060, 1107, 1111, 1123, 1142, 1189, 1209, 1239, 1259, 1278, 1315, 1341, 1339, 1369, 1399, 1407, 1422, 2092, 1473, 1474, 1497, 1502, 1531, 1590, 1589, 1673, 1679, 1681, 1704, 1700, 1708, 1786, 1803, 1833, 1834, 1835, 1898, 1910, 1919, 1381, 1918, 1958, 1965, 2035, 2053, 2075, 2080] identification [35, 62, 63, 438, 80, 81, 83, 89, 130, 145, 146, 176, 209, 224, 227, 233, 257, 308, 400, 401, 414, 418, 427, 436, 518, 517, 523, 585, 588, 630, 658, 677, 667, 691, 693, 728, 730, 736, 770, 768, 774, 861, 868, 875, 911, 953, 955, 1008, 1022, 1039, 1049, 1059, 1082, 1092, 1091, 1101, 1110, 1120, 1121, 1174, 1208, 1248, 1269, 1278, 1290, 1299, 1302, 1337, 1336, 1375, 1376, 1419, 1424, 1436, 1506, 1507, 1526, 1543, 1576, 1605, 1607, 1931, 1629, 1688, 1689, 1155, 1742, 1753, 1757, 1758, 1788, 1799, 1817, 1957, 1964, 2010, 2049, 2060, 2080] image [2, 8, 34, 35, 37, 40, 42, 46, 50, 51, 55, 56, 60, 61, 64, 72, 82, 88, 95, 103, 111, 122, 126, 133, 143, 150, 157, 168, 172, 178, 187, 193, 205, 206, 210, 211, 223, 222, 230, 232, 229, 241, 257, 262, 263, 2086, 2090, 276, 283, 281, 282, 285, 301, 767, 312, 324, 345, 346, 347, 359, 367, 379, 378, 388, 395, 408, 407, 417, 416, 423, 431, 441, 440, 446, 445, 455, 530, 537, 540, 546, 550, 567, 577, 585, 594, 593, 595, 604, 612, 618, 645, 646, 644, 662, 672, 678, 674, 673, 675, 684, 687, 688, 691, 692, 696, 699, 698, 709, 837, 835, 752, 761, 763, 765, 774, 784, 782, 783, 786, 806, 820, 822, 824, 845, 836, 831, 838, 848, 853, 860, 914, 921, 922, 923, 939, 961, 960, 962, 966, 967, 971, 968, 970, 969, 972, 974, 983, 1017, 1026, 1028, 1294, 1031, 1029, 1036, 1037, 1042, 1043, 1046, 1044, 1047, 1055, 1065, 1064, 1066, 1071, 1078, 1090, 1104, 1109, 1116, 1132, 1133, 1136, 1152, 1162, 1163, 1164, 1176, 1187, 1193, 1202, 1257, 1258, 1274, 1279, 1285, 1291, 1293, 1292, 1027, 1317, 1322, 1324, 1326, 1325, 1328, 1329, 1335, 1343, 1344, 1346, 1347, 1348, 1352, 1354, 1361, 1359, 1360, 1358, 1363, 1364, 1372, 1370, 1379, 1386, 1390, 1388, 1407, 1439, 1440, 1472, 1479, 1480, 1481, 1489, 1490, 1488, 1491, 1498, 1499, 1513, 1543, 1544, 1547, 1555, 1557, 1558, 1559, 1587, 1592, 1639, 1638, 1640, 1644, 1662, 1661, 1671, 1683, 1689, 1702, 1701, 1712, 1713, 1714, 1715, 1728, 1733, 1736, 1744, 1435, 1750, 1758, 1763, 1764, 1765, 1773, 1774, 1792, 1800, 1805, 1810, 1809, 1818, 1831, 1832, 1840, 1845, 1866, 1875, 1879, 1930, 1920, 1923, 1933, 1950, 1952, 1965, 1971, 1980, 1982, 1984, 2006, 2007, 2014, 2025, 2035, 2042, 2047, 2053, 2062, 2065, 2066, 2059, 2068, 2069, 2063, 2077, 2079, 2078] image analysis [35, 46, 2090, 281, 285, 359, 379, 662, 687, 774, 983, 1078, 1132, 1354, 1390, 1388, 1639, 1683, 1689, 1758, 1866, 1965] image classification [441, 763, 1026, 1046, 1055, 1116, 1439, 1440, 1555, 1671, 1832] image clustering [417] image coding [42, 82, 684, 1136, 1481, 1488, 1713, 1714, 1715, 1818, 2062, 2063, 2079, 2078] image compression [2, 60, 61, 82, 150, 172, 193, 540, 752, 784, 782, 1028, 1029, 1109, 1363, 1364, 1379, 1662, 1661, 1683, 1736, 2006, 2077] image database [168, 210, 229, 431, 970, 1317, 1322] image recognition [37, 407, 922, 1810, 1809, 2035] image retrieval [229, 416, 678, 966, 967, 971, 968, 969, 972, 1065, 1064, 1258, 1831, 1980, 2014, 2077] image segmentation [8, 126, 232, 262, 440, 585, 672, 674, 673, 675, 699, 822, 922, 961, 1017, 1026, 1294, 1044, 1078, 1202, 1274, 1292, 1027, 1335, 1435, 1930, 1920, 1971, 1984, 2025, 2035] image sequence [276, 835, 806, 836, 838, 921, 1479, 1543, 1587, 1644] imaging [9, 121, 352, 420, 423, 422, 455, 961, 960, 1290, 1375, 1498, 1499, 1574, 1643, 1661, 1734, 1920, 1970] impedance [1174, 1653, 1807] independent component analysis (ICA) [501, 721, 763, 1707, 2005] indexing [229, 363, 607, 843, 944, 945, 943, 844, 977, 1117, 1277, 1316, 1390, 1543, 1671, 2016, 2033, 2031, 2041, 2047, 2055] industrial [10, 24, 37, 438, 165, 395, 604, 667, 726, 731, 1005, 1251, 1498, 1633, 1636, 1631, 1805, 2050] infarction [352, 1471, 1470] information retrieval (IR) [561, 563, 642, 640, 750, 789, 944, 971, 1461, 1569, 1838] information-theoretic [1850, 1847] infrared (IR) [187, 267, 962, 1023, 1047, 1124, 1125, 1492, 1490, 1765] inheritance [702, 1907] integrated circuit [1509, 1516, 1759, 2043] intensity histogram [645, 646, 644] interference [2086, 414, 413, 498, 616, 656, 655, 706, 961, 960, 1017, 1392, 1679, 1836]
  9. Internet [200, 210, 215, 225, 250, 303, 344, 352, 363, 402, 657, 677, 703, 711, 904, 911, 978, 1001, 1243, 1289, 1422, 1444, 1449, 1463, 1577, 1621, 1863, 1862, 1861, 2011] interpolation [58, 131, 531, 532, 537, 547, 911, 947, 1086, 1188, 1390, 1392, 1486, 1545, 1546, 1739, 1825, 2002, 2038] intracardiac tachycardia [1606, 1605] invariant [8, 35, 48, 244, 276, 435, 618, 688, 693, 692, 823, 824, 1055, 1082, 1104, 1152, 1230, 1279, 1295, 1440, 1501, 1513, 1524, 1639, 1669, 1744, 1758, 1787, 1790, 1788, 1789] IR spectrometry [2018] isodata [556, 1017, 1750] jpeg [1029, 1543, 1662, 1661] Julier-Uhlmann-Kalman filter [727] k-means [88, 206, 352, 380, 400, 938, 1087, 1363, 1714, 1867, 1909, 1923]
  10. k-nearest neighbour classifier [113, 604, 760, 1534, 1684] Kalman filtering [575, 611] kanji recognition [2008] Karhunen-Loeve transform [150, 1744] kernel [365, 990, 1078, 1103, 1745, 1849, 1850, 1844, 1842, 1847, 1961, 1962, 1960] Klein bottle [1724] Kullback-Leibler [634, 633, 681, 2040, 2039]
  11. Landsat [69, 613, 612, 733, 1259, 1558, 1750, 1879] language [36, 130, 156, 353, 433, 477, 432, 563, 639, 640, 806, 904, 944, 1002, 1016, 1030, 1032, 1099, 1120, 1142, 1348, 1447, 2092, 1590, 1589, 1034, 1738, 1755, 1950, 2016, 2026] larvae [35, 1758] laser [85, 731, 1345] LBG [64, 82, 736, 1493, 1616, 1713, 1714, 2064] lexicon [1178] linguistic [6, 353, 389, 564, 580, 672, 673, 1056, 1120, 1312, 1349, 1350, 1607, 1620, 1665, 1777, 1780, 2080] lipid [689, 753] load forecasting [185, 186, 184, 183, 238, 615, 834, 1013, 1483, 1598, 1679, 2087, 2050] logistic regression [25, 218, 357, 950, 1732, 1961, 1962, 1960] LPC [1209, 1338, 1564, 1565, 1575] machine vision [760, 808, 935, 936, 1640, 1728, 1817] macromolecule [1372, 1371, 1370] magnetic resonance [101, 133, 243, 421, 423, 422, 781, 783, 1290, 1970, 1971] magnetosphere [1728] Mahalanobis distance [450, 1078, 1352, 1572] Manhattan distance [1154] market [91, 219, 237, 294, 320, 344, 329, 333, 337, 343, 510, 621, 1022, 1475, 1473, 1474, 1477, 1682, 1863, 1862, 1861, 2070] marketing [215, 337, 338, 1477, 1861] Markov model [149, 276, 275, 1026, 1119, 1278, 1497, 1965] maximum entropy [1707, 1847] medical [80, 81, 136, 193, 256, 359, 367, 411, 441, 440, 455, 547, 564, 590, 806, 868, 887, 911, 927, 1064, 1148, 1292, 1335, 1390, 1566, 1684, 1716, 1730, 1732, 1739, 1755, 1803, 2088, 1884, 1885, 1920, 1961, 1962] medical diagnosis [80, 81, 256, 547, 590, 911, 1335, 1684, 1732, 1961] medical image(s) [193, 359, 441, 440, 455] mesh [397, 1026, 1084, 1533, 2037] metabolic [401, 2061] metal [358, 413, 1405, 1418, 1717, 1977, 1976] meteorological [901, 902, 1036, 1037, 1068, 1175, 1199, 1728, 1764, 1952, 1973] microscopy images [960, 1163, 1164, 1291] mineralogical [1104] mitochondrial [1562] mixture density [681, 941, 942, 2089] mobile communication [144, 633] mobile robot [265, 266, 264, 494, 667, 727, 929, 930, 931, 1127, 1287, 1480, 1670, 1801, 1865] modulation [453, 618, 1336, 1431, 1690] molecular [15, 57, 73, 101, 119, 385, 426, 513, 538, 549, 689, 1010, 1115, 1399, 1414, 1432, 1502, 1563, 1754] money [914, 915, 913, 918, 916, 917, 912, 1328] monitoring [10, 24, 53, 52, 131, 134, 164, 240, 307, 358, 365, 414, 413, 428, 508, 509, 523, 526, 607, 611, 632, 655, 677, 721, 726, 747, 769, 770, 789, 805, 865, 903, 1006, 1024, 1038, 1050, 1060, 1162, 1204, 1390, 1448, 1495, 1516, 1556, 1564, 1587, 1607, 1628, 1717, 1735, 1740, 1746, 1757, 2087, 1849, 1854, 1866, 1876, 1912, 1939, 1938, 1940, 1955, 1956, 1977, 1976, 2056]
  12. Monte Carlo [141, 288, 948, 1093, 1264, 1094] morphology [283, 284, 281, 282, 285, 359, 448, 774, 845, 986, 1057, 1120, 1525, 1721, 1817] motion [266, 367, 421, 420, 434, 502, 546, 605, 253, 659, 837, 835, 836, 838, 905, 910, 909, 922, 986, 1239, 1266, 1283, 1284, 1346, 1560, 1585, 1619, 1624, 1670, 1807, 1808, 1983, 2041, 2079] motor [74, 157, 231, 243, 254, 365, 420, 494, 502, 746, 1110, 1114, 1220, 1239, 1212, 1224, 1226, 1228, 1213, 1217, 1229, 1231, 1233, 1218, 1266, 1265, 1326, 1325, 1421, 1426, 1425, 1538, 1735, 1992, 2074] motor control [746, 1220, 1212, 1224, 1226, 1217, 1233, 1218, 1266, 1538] MR images [126, 133, 359, 781, 783] multi-layer perceptron (MLP) [2, 38, 87, 117, 132, 133, 134, 156, 190, 189, 191, 203, 206, 216, 226, 240, 272, 305, 373, 392, 509, 534, 555, 604, 612, 627, 630, 656, 742, 758, 766, 786, 820, 901, 902, 904, 919, 986, 1022, 1042, 1077, 1140, 1164, 1246, 1268, 1274, 1332, 1341, 1339, 1382, 1404, 1406, 1427, 1488, 1519, 1531, 1542, 1575, 1598, 1615, 1625, 1681, 1684, 1749, 1753, 1811, 1814, 1815, 1817, 1824, 2087, 1834, 1835, 1854, 1900, 1961, 1959] multidimensional scaling [139, 866, 1527, 1528, 1578, 1864] multimedia [657, 656, 655, 710, 711, 708, 709, 905, 988, 1316, 1422, 1453, 1592] multiresolution [60, 61, 444, 1544, 1557] multiscale image [42, 126, 440] multisensor systems [272, 1077, 2089] multispectral [34, 40, 61, 88, 126, 379, 752, 1285, 1555, 1557, 1558, 1559, 1750, 1773, 1774, 1859, 1969, 1971] multispectral image [88, 752, 1555, 1558, 1559, 1773, 1774, 1971] music [180, 1186, 1316, 1431, 1453, 1663, 1691, 1705, 2046] myocardial infarction [1471, 1470] natural language [639, 640, 944, 1099, 1120, 1142, 1755] neurological [477, 1854] NP-complete [154, 268, 1674] nuclear power [1506, 1507] odor discrimination [509, 952, 951, 956, 1077, 1615] optical [146, 361, 446, 445, 472, 475, 509, 546, 669, 758, 921, 1259, 1345, 1406, 1422, 1498, 1520, 1627, 1700, 1705] orientation column [434, 1647] orientation map [252, 948, 1482, 1649] oscillators [618, 755, 756, 1907] oxide [627, 1077, 1121, 1336] parallel implementation [367, 906, 998, 1194, 1447, 1464, 1795, 1794, 2088, 1860]
  13. Parkinson's disease [149] particle [73, 178, 230, 419, 901, 974, 983, 1163, 1164] pathological [126, 352, 477, 534, 772, 773, 984, 1419] PCA [8, 21, 56, 117, 150, 153, 767, 328, 372, 415, 501, 509, 527, 763, 957, 983, 1023, 1077, 1113, 1149, 1152, 1156, 1259, 1361, 1360, 1492, 1489, 1491, 1557, 1558, 1559, 1592, 1637, 1657, 1689, 1875, 1909, 1914, 1957, 2056] Peano scan [1774] peptides [1562] person identification [1419] PET image [1845] phoneme [29, 279, 512, 941, 1151, 1278, 1426, 1497, 1690, 1806] phonocardiogram [2046] photoelectron spectroscopy [1309, 1827] PICSOM [966, 967, 971, 968, 970, 969, 972] plant [425, 435, 436, 518, 517, 729, 741, 816, 947, 1929, 1203, 1204, 1252, 1250, 1409, 1506, 1507, 1532, 1599, 1817, 1833, 1834, 1835] plasma [90, 753, 1728, 1910] polarimetric [212, 593, 661] polymer [245, 928, 1245, 1827] posture [408, 805, 853, 1216, 1302, 2092, 1538] power system [52, 239, 246, 247, 313, 314, 474, 508, 615, 732, 1000, 1007, 1093, 1092, 1091, 1204, 1269, 1468, 1467, 1485, 1507, 1651, 1650, 1652, 1757, 1964, 1094, 1996, 2021, 2045] prediction [5, 6, 15, 25, 33, 41, 53, 57, 87, 100, 101, 106, 107, 130, 171, 175, 185, 186, 184, 183, 218, 223, 233, 239, 240, 307, 328, 334, 357, 385, 390, 401, 413, 426, 636, 509, 517, 525, 538, 547, 549, 560, 576, 596, 615, 628, 635, 655, 659, 687, 731, 746, 757, 788, 843, 856, 919, 950, 844, 1929, 1056, 1067, 1077, 1140, 1168, 1176, 1199, 1204, 1209, 1208, 1216, 1256, 1254, 1267, 1268, 1280, 1283, 1284, 1304, 1374, 1397, 1399, 1414, 1483, 1560, 1564, 1565, 1591, 1629, 1645, 1686, 1687, 1716, 1732, 1745, 1749, 1811, 1826, 1834, 1835, 1887, 1894, 1967, 1973, 1974, 1990, 2032, 2048, 2050, 2056, 2081] preprocessing [21, 110, 274, 276, 446, 445, 501, 707, 721, 763, 955, 957, 1008, 1011, 1046, 1068, 1171, 1189, 1348, 1466, 1477, 1489, 1490, 1491, 1654, 1799, 2053] probabilistic [3, 10, 23, 34, 39, 90, 213, 219, 327, 439, 441, 553, 634, 633, 728, 873, 879, 955, 1086, 1096, 1151, 1252, 1305, 1356, 1397, 2092, 1548, 1553, 1767, 1852] probabilistic neural network (PNN) [90, 117, 439, 441, 728, 955, 1096, 1305, 1356, 1764, 1765] projection pursuit [69, 1840, 1845] protein sequences [449, 671, 890, 1568, 1668, 1666] pruning [117, 138, 418, 443, 719, 720, 1081, 1303, 1819, 1865, 1981, 2041]
  14. PSOM [1486, 1914] psychiatric [477] psychological [100, 1431, 1888, 1887] psychovisual [444] pulp [24, 402, 903, 1632, 1866, 1873] PVM [76, 1080, 1447] QAM [1, 1048, 1442, 2089, 1934, 1935] quantum computation [1035] quartz-resonator [951] questionnaires [1015, 1888, 1887] radar [46, 324, 400, 412, 593, 692, 707, 771, 1036, 1037, 1124, 1125, 1390, 1389, 1544, 1712, 1793, 1814, 1948, 1952, 1985] radiology [241, 367] Raman spectrometry [683, 2019] radial-basis-function (RBF) [16, 38, 87, 88, 125, 131, 144, 156, 222, 233, 277, 355, 384, 454, 484, 630, 728, 742, 780, 922, 1192, 1332, 1333, 1415, 1427, 1988, 1506, 1507, 1540, 1582, 1579, 1753, 1770, 1790, 1788, 1789, 1811, 1903, 1959, 2056, 2073] receiver operating characteristic (ROC) [223, 1290] recurrent [74, 130, 525, 649, 648, 655, 755, 756, 919, 987, 986, 1049, 1110, 1142, 1240, 1274, 1483, 1497, 1506, 1507, 1524, 1690, 1856, 1857, 1855, 1901, 1907] regression [25, 218, 357, 365, 399, 418, 423, 635, 758, 805, 865, 902, 950, 1112, 1304, 1311, 1356, 1566, 1679, 1732, 1840, 1845, 1932, 1961, 1962, 1960, 1978, 2050, 2073] reinforcement learning [4, 45, 584, 583, 713, 864, 963, 1041, 1114, 1706, 1801, 2052, 1508] REM sleep [99, 559] remote-sensing images [1965] retrieval [50, 68, 128, 200, 229, 353, 416, 443, 547, 562, 563, 600, 603, 607, 608, 642, 640, 657, 678, 710, 711, 708, 709, 789, 821, 911, 944, 945, 966, 967, 971, 968, 970, 969, 972, 976, 977, 1065, 1064, 1099, 1117, 1170, 1177, 1258, 1277, 1317, 1316, 1322, 1453, 1461, 1460, 2092, 1569, 1663, 1673, 1831, 1838, 1895, 1980, 2014, 2032, 2034, 2033, 2031, 1076, 2077]
  15. RGB [301, 352, 604, 678, 760, 848, 935, 936, 1017, 1324, 1774] robotics [45, 102, 106, 158, 157, 195, 265, 266, 264, 358, 377, 446, 494, 521, 580, 605, 667, 727, 743, 746, 929, 930, 931, 963, 1024, 1069, 1071, 1114, 1126, 1127, 1148, 1191, 1239, 1238, 1271, 1287, 1402, 1429, 1070, 1446, 1480, 1484, 1486, 1539, 1670, 1683, 1690, 1706, 1753, 1770, 1769, 1802, 1801, 1805, 1807, 1865, 1900, 1914, 1508, 2074] robust [38, 45, 64, 77, 79, 106, 164, 203, 208, 219, 275, 379, 378, 412, 414, 426, 446, 445, 447, 453, 460, 457, 530, 602, 680, 667, 743, 759, 786, 805, 825, 828, 868, 922, 959, 1054, 1082, 1087, 1109, 1116, 1120, 1123, 1132, 1149, 1151, 1201, 1200, 1209, 1287, 1376, 1401, 1449, 2092, 1477, 1506, 1507, 1533, 1548, 1575, 1588, 1606, 1605, 1608, 1656, 1673, 1677, 1735, 1806, 1811, 2087, 1834, 1835, 1862, 1914, 1948, 1995, 2006, 2040, 2041, 2043, 2047, 2062, 2084, 2083, 2085] Sammon mapping [1130, 1129, 1154, 1455, 1555, 1867, 2037] satellite [34, 143, 144, 146, 204, 255, 2090, 612, 662, 863, 962, 1046, 1285, 1324, 1422, 1423, 1557, 1629, 1661, 1764, 1765, 1877, 1879, 1910, 1909, 1994, 2055] schizophrenia [477] segmentation [8, 9, 30, 51, 126, 133, 168, 232, 237, 243, 251, 252, 262, 269, 284, 282, 352, 357, 439, 440, 455, 512, 531, 532, 585, 604, 672, 674, 673, 675, 693, 697, 696, 695, 699, 837, 835, 781, 783, 822, 845, 836, 838, 909, 922, 924, 961, 960, 998, 1017, 1026, 1294, 1031, 1044, 1075, 1078, 1096, 1202, 1274, 1285, 1291, 1293, 1292, 1027, 1315, 1329, 1335, 1344, 1408, 1477, 1514, 1515, 350, 1543, 1608, 1644, 1643, 1665, 1666, 1435, 2089, 1863, 1862, 1861, 1930, 1986, 1920, 1936, 1937, 1965, 1969, 1971, 1984, 2008, 2025, 2035, 2041, 2047, 2070] seismic [32, 427, 728, 1138, 1281, 1280, 1742] seizure [497, 723, 950] self-organizing perceptron (SOP) [609, 610] self-supervised [1218, 1539, 1905, 1906] semantic [7, 27, 54, 175, 304, 370, 420, 572, 641, 835, 815, 813, 814, 812, 944, 945, 943, 977, 1002, 1098, 1099, 1162, 1403, 1097, 1621, 1755, 1831, 1893, 2012, 2031] sentence [108, 175, 353, 572, 736, 1142, 1690, 1895, 1897] sequence [33, 349, 134, 159, 182, 179, 181, 197, 217, 221, 220, 228, 268, 273, 277, 274, 276, 275, 376, 382, 411, 446, 445, 449, 494, 530, 532, 546, 553, 565, 580, 584, 583, 602, 655, 671, 739, 835, 743, 755, 764, 782, 806, 836, 838, 921, 929, 992, 1035, 1036, 1037, 1071, 1142, 1145, 1195, 1256, 1254, 1255, 1285, 1327, 1402, 1426, 1425, 1434, 1469, 1470, 1479, 1480, 1507, 1543, 1587, 1644, 1653, 1668, 1666, 1667, 1663, 1664, 1718, 1806, 1856, 1857, 1875, 1897, 1952, 1433, 1993, 1995, 2008, 1719, 2041] ship [662, 707, 1080, 1376] signal processing [136, 525, 691, 728, 1209, 1336, 1554, 1556, 1811, 1814, 2088, 1955, 1974, 2005, 2065] signature [95, 400, 515, 555, 769, 768, 771, 1055, 1080, 1124, 1125, 1279, 1786, 1958] silicon [501, 994, 1336, 1418] SIMD [1109, 1722] simulated annealing [148, 189, 381, 2062] sleep [99, 539, 559, 748, 1205, 1970, 2056] software [199, 283, 336, 338, 391, 423, 636, 455, 520, 557, 560, 581, 834, 709, 805, 904, 905, 1110, 1253, 1285, 1355, 1378, 1384, 1449, 1464, 1500, 1509, 1530, 1668, 1672, 1722, 1732, 1795, 1794, 2087, 1833, 1874, 1897, 2032, 2033, 2031] solder joint(s) [840, 1499, 2051] sonar [14, 245, 739, 860, 1079, 1127, 1865, 2025] sonogram [223] sparse [528, 572, 687, 688, 842, 904, 905, 998, 1397, 1639, 1640] speaker [110, 145, 305, 452, 736, 953, 955, 1209, 1208, 1278, 1667, 1690, 1824, 1893, 2000] spectrofluorometric [173, 1772, 1772] spectrometry [683, 903, 1122, 1853, 2018, 2019] spectroscopy [542, 753, 1242, 1243, 1309, 1780] spectrum [60, 61, 64, 90, 101, 145, 146, 173, 232, 255, 267, 304, 361, 395, 515, 524, 539, 550, 567, 604, 618, 660, 683, 691, 733, 739, 751, 752, 760, 772, 773, 805, 863, 903, 935, 936, 955, 1038, 1929, 1046, 1104, 1162, 1208, 1242, 1243, 1252, 1250, 1276, 1281, 1285, 1309, 1312, 1310, 1314, 1306, 1324, 1336, 1371, 1370, 1375, 1404, 1419, 1470, 1641, 1690, 1744, 1750, 1765, 1777, 1780, 569, 1811, 1826, 1827, 1854, 1859, 1888, 1433, 1993, 1994, 2055] speech [29, 80, 81, 108, 110, 130, 2086, 279, 305, 309, 362, 433, 451, 432, 503, 512, 533, 746, 772, 773, 778, 908, 932, 944, 945, 942, 953, 955, 958, 1069, 1151, 1185, 1235, 1276, 1278, 1338, 1426, 1070, 1434, 2092, 1497, 1531, 1575, 1590, 1589, 1665, 1666, 1667, 1663, 1690, 1806, 2089, 1847, 1860] speech recognition [29, 80, 81, 108, 130, 2086, 305, 451, 533, 773, 944, 942, 955, 958, 1276, 1497, 1575, 1590, 1589, 1665, 1666, 1667, 1663, 1690, 1806, 2089] speech signals [772, 773, 932, 1185, 1338, 1847] splines [1257] stereovision [1352] stock market [91, 344, 337] string [108, 148, 456, 580] stroke [257, 1375] subspace [8, 21, 201, 208, 2086, 415, 688, 721, 722, 789, 923, 944, 1146, 1152, 1193, 2092, 1514, 1513, 1657, 2089, 1847, 2000, 2030, 2057, 1076] subsymbolic [815, 814, 1177] surface inspection [697, 698, 809, 808, 810] surgery [149] system identification [62, 63, 418, 518, 517, 1049, 1248, 1424, 1605, 1155] tachycardia [1469, 1606, 1605, 1943] telecommunication [12, 2086, 309, 1633, 1632, 1743, 1947] temporal [50, 54, 87, 95, 208, 216, 217, 231, 243, 421, 423, 430, 433, 434, 464, 477, 482, 496, 519, 564, 565, 618, 652, 649, 648, 755, 842, 909, 929, 930, 931, 933, 987, 986, 1033, 1066, 1131, 1369, 1384, 1426, 1425, 1434, 1510, 1511, 1512, 1544, 1560, 1665, 1663, 1664, 1677, 1762, 1830, 1856, 1857, 1855, 1901, 1919, 1916, 1918, 2052, 1983] temporal sequence [217, 565, 755, 929, 1426, 1425, 1434, 1663, 1857] TEXSOM [1515, 350] text [7, 6, 8, 67, 68, 304, 305, 353, 433, 452, 432, 528, 529, 601, 642, 754, 790, 789, 821, 886, 894, 878, 896, 976, 977, 978, 979, 1002, 1001, 1016, 1045, 1055, 1129, 1158, 1170, 1169, 1166, 1179, 1274, 1327, 1403, 1430, 1464, 1463, 1453, 1455, 1451, 1458, 1504, 1505, 1577, 1688, 1689, 1738, 1755, 1795, 1824, 1896, 1895, 1897, 1980, 2015, 2016, 2017] text document [6, 642, 789, 896, 979, 1403, 1430, 1505, 1896, 1895, 1897, 2015, 2016] textile [72, 428, 786, 2048] texture [8, 21, 46, 50, 51, 56, 134, 168, 204, 208, 223, 232, 255, 257, 2086, 387, 558, 589, 604, 697, 696, 695, 698, 835, 760, 836, 838, 860, 924, 936, 962, 966, 971, 1065, 1064, 1067, 1078, 1133, 1134, 1139, 1148, 1202, 1258, 1292, 1316, 1359, 1360, 1358, 1375, 1407, 1408, 1514, 1515, 350, 1638, 1689, 1712, 1744, 1763, 1791, 1831, 1837, 1986, 1936, 1937, 1965, 1978] texture classification [51, 1407, 1744, 1791, 1837] texture features [8, 204, 223, 387, 612, 697, 696, 698, 835, 786, 836, 962, 1065, 1064, 1067, 1375, 1407, 1408, 1688, 1764, 1765, 1831, 1978] texture segmentation [232, 604, 695, 924, 1202, 1408, 1514, 1515, 350, 1986, 1936, 1937] therapy [447, 868, 1140, 1876, 1888, 1887] time series [69, 87, 107, 130, 180, 238, 423, 474, 482, 525, 564, 902, 919, 987, 986, 1013, 1060, 1251, 1474, 1686, 1687, 1720, 1745, 1812, 1822, 1887] time-frequency [413, 1019, 1717, 1816] tissue [243, 269, 367, 531, 550, 783, 1086, 1139, 1521] tomography [359, 1065, 1479, 1800] tracking [102, 103, 437, 551, 611, 727, 910, 947, 974, 999, 1237, 1376, 1390, 1431, 2092, 1793, 2041, 196] trading [344, 329, 1251, 1475, 1473, 1474, 1682] traffic [207, 226, 498, 526, 611, 657, 703, 705, 704, 1444, 1627] transputer [994] traveling salesman problem (TSP) [31, 47, 48, 154, 268, 487, 486, 492, 488, 490, 830, 846, 934, 1106, 1611, 1776, 1898] tree [56, 82, 150, 165, 172, 204, 203, 210, 211, 221, 220, 283, 284, 281, 376, 375, 431, 443, 522, 596, 612, 626, 666, 671, 670, 716, 900, 904, 906, 907, 966, 967, 971, 968, 969, 1138, 1170, 1168, 1258, 1309, 1306, 1307, 1308, 1316, 1337, 1360, 1366, 1390, 1387, 1389, 1388, 1519, 1566, 1639, 1684, 1745, 1762, 1767, 1837, 1908, 1986, 1936, 1937, 2054] tree structured SOM (TS-SOM) [211, 581, 900, 966, 971, 968, 969, 1837] turbines [721] U-matrix [194, 283, 284, 282, 285, 2037] ultrasonic [176, 244, 257, 256, 245, 922, 1305, 1375, 1643, 1670] unemployment [293] urologic [547] usability [79, 526, 557] varimax [1113] vitality conservation (VC) network [1923] vehicle [366, 1125, 1378, 1674] video [50, 221, 220, 223, 277, 275, 408, 444, 601, 600, 656, 905, 910, 909, 1071, 1302, 1316, 1346, 1369, 2092, 1810, 2088, 2041, 2047, 2079] virtual-reality [1786] viscosity [1220, 1538, 1807] vision [74, 103, 168, 197, 312, 589, 674, 675, 760, 786, 809, 808, 864, 935, 936, 939, 1071, 2092, 1560, 1640, 1678, 1683, 1728, 1791, 1817, 1923, 1950] visual cortex [74, 100, 434, 444, 614, 757, 1009, 1146, 1180, 1181, 1396, 1619, 1624, 1649, 1647, 1724, 1723, 1914, 1950] visualization [24, 27, 37, 60, 65, 131, 142, 153, 166, 199, 283, 284, 281, 285, 307, 337, 352, 354, 365, 398, 401, 439, 447, 468, 467, 470, 484, 491, 489, 506, 508, 520, 526, 529, 557, 581, 622, 621, 626, 625, 629, 631, 642, 639, 640, 666, 682, 833, 754, 764, 804, 800, 797, 801, 790, 805, 825, 855, 862, 863, 866, 887, 890, 897, 880, 933, 944, 960, 976, 977, 978, 983, 989, 1000, 1086, 1096, 1104, 1129, 1160, 1169, 1171, 1174, 1184, 1242, 1243, 1245, 1251, 1264, 1270, 1286, 1304, 1309, 1335, 1374, 1378, 1390, 1409, 1418, 1432, 1443, 1448, 1455, 1451, 1467, 1470, 1493, 1500, 1503, 1516, 1530, 1558, 1559, 1564, 1565, 1578, 1603, 1632, 1643, 1668, 1665, 1696, 1694, 1739, 1750, 1754, 1768, 1767, 1778, 1849, 1863, 1862, 1861, 1864, 1869, 1873, 1872, 1867, 1876, 1884, 1885, 1911, 1971, 2011, 2012, 2037] visuo-motor [157, 481, 746, 1326, 1325, 2074] Viterbi [437, 1965] VLSI [926, 1089, 1087, 1088, 1109, 1511, 1527, 1528, 1606, 1605, 1883, 1903] voice [64, 166, 181, 534, 572, 656, 765, 1012, 1186, 1338, 1566] volumetry [9, 1789, 1969, 2083, 2085] vowel [29, 166, 484, 1590, 1589] voxel [423, 422, 1971] VRML [352] W-CDMA [657, 656] wafer-scale integration [2027] Ward's clustering [1505] wavelet [170, 205, 206, 232, 254, 444, 768, 786, 1011, 1033, 1046, 1110, 1138, 1251, 1557, 1592, 1691, 1764, 1765, 1837, 1847, 1854] weather [255, 962, 999, 1039, 1068, 1125, 1175, 1199, 1390, 1389, 1679, 1973, 2050]
  16. WEBSOM [67, 68, 250, 467, 642, 793, 789, 976, 975, 978, 1016, 1403, 1838] Wigner distribution [1814, 1816] word [29, 54, 67, 108, 145, 175, 305, 345, 346, 347, 420, 451, 530, 547, 610, 709, 845, 886, 894, 872, 908, 944, 958, 976, 999, 1002, 1016, 1130, 1151, 1206, 1276, 1334, 1403, 1416, 1426, 1097, 1479, 1690, 1755, 1806, 1838, 1893, 1896, 1895, 1897, 2015, 2055]
  17. WWW [210, 352, 607, 710, 711, 708, 709, 1271, 1348, 1991] X-ray [176, 385, 1242, 1243, 1309, 1312, 1498, 1499, 1715, 1777, 1827] yeast fermentation [401] Z-analysis [1594] REFERENCES
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