@prefix rdf: . @prefix dbpedia: . @prefix ns2: . dbpedia:Self-organizing_map rdf:type ns2:NeuralNetworks . @prefix owl: . dbpedia:Self-organizing_map owl:sameAs . @prefix foaf: . @prefix ns5: . dbpedia:Self-organizing_map foaf:page ns5:Self-organizing_map . @prefix dbpprop: . dbpedia:Self-organizing_map dbpprop:reference , . @prefix ns7: . dbpedia:Self-organizing_map dbpprop:reference ns7:somine . @prefix ns8: . dbpedia:Self-organizing_map dbpprop:reference ns8:self-organizing-map , , , , , , . @prefix ns9: . dbpedia:Self-organizing_map dbpprop:reference ns9:the-self-organized-gene-part-1 , , . @prefix ns10: . dbpedia:Self-organizing_map dbpprop:reference ns10:the-self-organized-gene-part-2 , , , , , , . @prefix rdfs: . dbpedia:Self-organizing_map rdfs:label "Self-Organizing Map"@it , "Self-organizing map"@en , "Carte auto adaptative"@fr , "\u0421\u0430\u043C\u043E\u043E\u0440\u0433\u0430\u043D\u0456\u0437\u0430\u0446\u0456\u0439\u043D\u0430 \u041A\u0430\u0440\u0442\u0430 \u041A\u043E\u0445\u043E\u043D\u0435\u043D\u0430"@uk , "\u0421\u0430\u043C\u043E\u043E\u0440\u0433\u0430\u043D\u0438\u0437\u0443\u044E\u0449\u0430\u044F\u0441\u044F \u043A\u0430\u0440\u0442\u0430 \u041A\u043E\u0445\u043E\u043D\u0435\u043D\u0430"@ru , "Mapa autoorganizado"@es , "\u81EA\u5DF1\u7D44\u7E54\u5316\u5199\u50CF"@ja , "Selbstorganisierende Karte"@de , "Kohonen-netwerk"@nl , "Sie\u0107 Kohonena"@pl , "Itseorganisoituva kartta"@fi . @prefix dbpedia-owl: . dbpedia:Self-organizing_map dbpedia-owl:thumbnail ; dbpprop:abstract "\u0421\u0430\u043C\u043E\u043E\u0440\u0433\u0430\u043D\u0438\u0437\u0443\u044E\u0449\u0430\u044F\u0441\u044F \u043A\u0430\u0440\u0442\u0430 \u041A\u043E\u0445\u043E\u043D\u0435\u043D\u0430 (\u0430\u043D\u0433\u043B. Self-organizing map\u00A0\u2014 SOM)\u00A0\u2014 \u0441\u043E\u0440\u0435\u0432\u043D\u043E\u0432\u0430\u0442\u0435\u043B\u044C\u043D\u0430\u044F \u043D\u0435\u0439\u0440\u043E\u043D\u043D\u0430\u044F \u0441\u0435\u0442\u044C \u0441 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435\u043C \u0431\u0435\u0437 \u0443\u0447\u0438\u0442\u0435\u043B\u044F, \u0432\u044B\u043F\u043E\u043B\u043D\u044F\u044E\u0449\u0430\u044F \u0437\u0430\u0434\u0430\u0447\u0443 \u0432\u0438\u0437\u0443\u0430\u043B\u0438\u0437\u0430\u0446\u0438\u0438 \u0438 \u043A\u043B\u0430\u0441\u0442\u0435\u0440\u0438\u0437\u0430\u0446\u0438\u0438. \u0418\u0434\u0435\u044F \u0441\u0435\u0442\u0438 \u043F\u0440\u0435\u0434\u043B\u043E\u0436\u0435\u043D\u0430 \u0444\u0438\u043D\u0441\u043A\u0438\u043C \u0443\u0447\u0451\u043D\u044B\u043C \u0422. \u041A\u043E\u0445\u043E\u043D\u0435\u043D\u043E\u043C. \u042F\u0432\u043B\u044F\u0435\u0442\u0441\u044F \u043C\u0435\u0442\u043E\u0434\u043E\u043C \u043F\u0440\u043E\u0435\u0446\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u043C\u043D\u043E\u0433\u043E\u043C\u0435\u0440\u043D\u043E\u0433\u043E \u043F\u0440\u043E\u0441\u0442\u0440\u0430\u043D\u0441\u0442\u0432\u0430 \u0432 \u043F\u0440\u043E\u0441\u0442\u0440\u0430\u043D\u0441\u0442\u0432\u043E \u0441 \u0431\u043E\u043B\u0435\u0435 \u043D\u0438\u0437\u043A\u043E\u0439 \u0440\u0430\u0437\u043C\u0435\u0440\u043D\u043E\u0441\u0442\u044C\u044E (\u0447\u0430\u0449\u0435 \u0432\u0441\u0435\u0433\u043E, \u0434\u0432\u0443\u043C\u0435\u0440\u043D\u043E\u0435), \u043F\u0440\u0438\u043C\u0435\u043D\u044F\u0435\u0442\u0441\u044F \u0442\u0430\u043A\u0436\u0435 \u0434\u043B\u044F \u0440\u0435\u0448\u0435\u043D\u0438\u044F \u0437\u0430\u0434\u0430\u0447 \u043C\u043E\u0434\u0435\u043B\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F, \u043F\u0440\u043E\u0433\u043D\u043E\u0437\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u0438 \u0434\u0440. \u042F\u0432\u043B\u044F\u0435\u0442\u0441\u044F \u043E\u0434\u043D\u043E\u0439 \u0438\u0437 \u0432\u0435\u0440\u0441\u0438\u0439 \u043D\u0435\u0439\u0440\u043E\u043D\u043D\u044B\u0445 \u0441\u0435\u0442\u0435\u0439 \u041A\u043E\u0445\u043E\u043D\u0435\u043D\u0430."@ru , "Een Kohonen-netwerk, zelf organiserend netwerk of self-Organizing Maps is een kunstmatig neuraal netwerk bedacht door de Finse professor Teuvo Kohonen."@nl , "\u0421\u0430\u043C\u043E\u043E\u0440\u0433\u0430\u043D\u0456\u0437\u0430\u0446\u0456\u0439\u043D\u0430 \u043A\u0430\u0440\u0442\u0430 \u041A\u043E\u0445\u043E\u043D\u0435\u043D\u0430 (\u0430\u043D\u0433\u043B. Self-organizing map \u2014 SOM) \u2014 \u043D\u0435\u0439\u0440\u043E\u043D\u043D\u0430 \u043C\u0435\u0440\u0435\u0436\u0430 \u0437 \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u044F\u043C \u0437\u0456 \u0437\u043C\u0430\u0433\u0430\u043D\u043D\u044F\u043C \u0431\u0435\u0437 \u0432\u0447\u0438\u0442\u0435\u043B\u044F, \u0449\u043E \u0432\u0438\u043A\u043E\u043D\u0443\u0454 \u0437\u0430\u0432\u0434\u0430\u043D\u043D\u044F \u043A\u043B\u0430\u0441\u0442\u0435\u0440\u0438\u0437\u0430\u0446\u0456\u0457. \u0404 \u043C\u0435\u0442\u043E\u0434\u043E\u043C \u043F\u0440\u043E\u0435\u043A\u0442\u0443\u0432\u0430\u043D\u043D\u044F \u0431\u0430\u0433\u0430\u0442\u043E\u0432\u0438\u043C\u0456\u0440\u043D\u043E\u0433\u043E \u043F\u0440\u043E\u0441\u0442\u043E\u0440\u0443 \u0432 \u043F\u0440\u043E\u0441\u0442\u0456\u0440 \u0437 \u043D\u0438\u0436\u0447\u043E\u044E \u0440\u043E\u0437\u043C\u0456\u0440\u043D\u0456\u0441\u0442\u044E (\u043D\u0430\u0439\u0447\u0430\u0441\u0442\u0456\u0448\u0435, \u0434\u0432\u043E\u0432\u0438\u043C\u0456\u0440\u043D\u0435), \u0437\u0430\u0441\u0442\u043E\u0441\u043E\u0432\u0443\u0454\u0442\u044C\u0441\u044F \u0442\u0430\u043A\u043E\u0436 \u0434\u043B\u044F \u0432\u0438\u0440\u0456\u0448\u0435\u043D\u043D\u044F \u0437\u0430\u0432\u0434\u0430\u043D\u044C \u043C\u043E\u0434\u0435\u043B\u044E\u0432\u0430\u043D\u043D\u044F, \u043F\u0440\u043E\u0433\u043D\u043E\u0437\u0443\u0432\u0430\u043D\u043D\u044F \u0442\u0430 \u0456\u043D\u0448\u0438\u0445."@uk , "Itseorganisoiva kartta (engl. Self-Organizing Map, SOM) on akateemikko Teuvo Kohosen kehitt\u00E4m\u00E4 ohjaamattomaan oppimiseen perustuva neuroverkkomalli, jonka h\u00E4n julkaisi 1980-luvulla. Se on Kohosen kansainv\u00E4lisesti tunnetuin ty\u00F6. Mallista on k\u00E4ytetty my\u00F6s nime\u00E4 Kohonen map/network. Itseorganisoivassa kartassa tilastolliset yhteydet moniulotteisen datajoukon alkioiden v\u00E4lill\u00E4 muunnetaan yksinkertaisiksi geometrisiksi suhteiksi, jotka voidaan n\u00E4ytt\u00E4\u00E4 esimerkiksi kaksiulotteisena karttana. Moniulotteinen tieto siis pakataan tavalla, jossa t\u00E4rkeimm\u00E4t topologiset ja metriset suhteet alkioiden v\u00E4lill\u00E4 s\u00E4ilyv\u00E4t, ja lopputuloksena syntyv\u00E4 kartta voi tarjota jonkinlaisen abstraktion tietosis\u00E4lt\u00F6\u00F6n. Itseorganisoivan kartan sovelluksia ovat mm. puheen- ja hahmontunnistus, visualisointi, tietoliikennetekniikka ja semanttinen web."@fi , "Le self-organizing map (SOM) sono un particolare tipo di rete neurale artificiale. \u00C8 addestrata usando l'apprendimento non supervisionato per produrre una rappresentazione dei campioni di training in uno spazio a bassa dimensione preservando le propriet\u00E0 topologiche dello spazio degli ingressi. Questa propriet\u00E0 rende le SOM particolarmente utili per la visualizzazione di dati di dimensione elevata. Il modello fu inizialmente descritto dal professore finlandese Teuvo Kohonen e spesso ci si riferisce a questo modello come Mappe di Kohonen."@it , "Carte auto adaptative ou auto organisatrice est une classe de r\u00E9seau de neurones artificiels fond\u00E9e sur des m\u00E9thodes d'apprentissage non supervis\u00E9e. On la d\u00E9signe souvent par le terme anglais self organizing map (SOM), on encore carte de Kohonen du nom du statisticien ayant d\u00E9velopp\u00E9 le concept en 1984. Elles sont utilis\u00E9es pour cartographier un espace r\u00E9el, c'est-\u00E0-dire pour \u00E9tudier la r\u00E9partitions de donn\u00E9es dans un espace \u00E0 grande dimension. En pratique, cette cartographie peut servir \u00E0 r\u00E9aliser des t\u00E2ches de discr\u00E9tisation, quantification vectorielle, ou classification (voir un exemple sur le site pour la discr\u00E9tisation de l'espace de travail d'un robot)."@fr , "Los mapas autoorganizados o SOM (Self-Organizing Map), tambi\u00E9n llamados redes de Kohonen son un tipo de red neuronal no supervisada, competitiva, distribuida de forma regular en una rejilla de, normalmente, dos dimensiones, cuyo fin es descubrir la estructura subyacente de los datos introducidos en ella. A lo largo del entrenamiento de la red, los vectores de datos son introducidos en cada neurona y se comparan con el vector de peso caracter\u00EDstico de cada neurona. La neurona que presenta menor diferencia entre su vector de peso y el vector de datos es la neurona ganadora (o BMU) y ella y sus vecinas ver\u00E1n modificados sus vectores de pesos."@es , "\u81EA\u5DF1\u7D44\u7E54\u5316\u5199\u50CF\uFF08\u3058\u3053\u305D\u3057\u304D\u304B\u3057\u3083\u305E\u3046, \u82F1\u8A9E:Self-organizing maps, SOM\uFF09\u306F \u5927\u8133\u76AE\u8CEA\u306E\u8996\u899A\u91CE\u3092\u30E2\u30C7\u30EB\u5316\u3057\u305F\u30CB\u30E5\u30FC\u30E9\u30EB\u30CD\u30C3\u30C8\u306E\u4E00\u7A2E\u3067\u3042\u308B\u3002 \u6559\u5E2B\u306A\u3057\u5B66\u7FD2\u306B\u3088\u308B\u30AF\u30E9\u30B9\u30BF\u30EA\u30F3\u30B0\u306E\u624B\u6CD5\u306E\u4E00\u3064\u3067\u3042\u308B\u3002 \u6B21\u5143\u524A\u6E1B\u306B\u3088\u308B\u53EF\u8996\u5316\u306E\u624B\u6CD5\u306E\u4E00\u3064\u3067\u3042\u308B\u3002 \u81EA\u5DF1\u7D44\u7E54\u5316\u30DE\u30C3\u30D7\u3068\u3082\u547C\u3070\u308C\u308B\u3002 \u4EBA\u5DE5\u30CB\u30E5\u30FC\u30ED\u30F3\u3092\u683C\u5B50\u72B6\u306B\u914D\u7F6E\u3057\u3001\uFF08\u5165\u529B\u5C64\u304B\u3089\u306E\uFF09\u30B7\u30CA\u30D7\u30B9\u7D50\u5408\u306E\u91CD\u307F\u3092\u5B66\u7FD2\u3059\u3079\u304D\u5165\u529B\u30D9\u30AF\u30C8\u30EB\u306E\u96C6\u5408\uFF08\u30C8\u30EC\u30FC\u30CB\u30F3\u30B0\u30BB\u30C3\u30C8\uFF09\u3068\u9069\u5408\u3059\u308B\u3088\u3046\u306B\u5909\u5316\u3055\u305B\u308B\u3002 \u30B3\u30DB\u30CD\u30F3\uFF08\u30B3\u30DB\u30FC\u30CD\u30F3\uFF09\u304C\u6700\u521D\u306B\u63D0\u6848\u3057\u305F\u306E\u3067\u3001\u30B3\u30DB\u30CD\u30F3\u30DE\u30C3\u30D7\uFF08\u30B3\u30DB\u30FC\u30CD\u30F3\u30DE\u30C3\u30D7\u3001\u30B3\u30DB\u30FC\u30CD\u30F3\u30CD\u30C3\u30C8\u30EF\u30FC\u30AF\uFF09\u3068\u3082\u547C\u3070\u308C\u308B\u3002"@ja , "Als Selbstorganisierende Karten, Kohonenkarten oder Kohonennetze bezeichnet man eine Art von k\u00FCnstlichen neuronalen Netzen. Sie sind als un\u00FCberwachtes Lernverfahren ein leistungsf\u00E4higes Werkzeug des Data-Mining. Ihr Funktionsprinzip beruht auf der biologischen Erkenntnis, dass viele Strukturen im Gehirn eine lineare oder planare Topologie aufweisen. Die Signale des Eingangsraums, z. B. visuelle Reize, sind jedoch multidimensional. Es stellt sich also die Frage, wie diese multidimensionalen Eindr\u00FCcke durch planare Strukturen verarbeitet werden. Biologische Untersuchungen zeigen, dass die Eingangssignale so abgebildet werden, dass \u00E4hnliche Reize nahe beieinander liegen. Der Phasenraum der angelegten Reize wird also kartiert. Wird nun ein Signal an diese Karte herangef\u00FChrt, so werden nur diejenigen Gebiete der Karte erregt, die dem Signal \u00E4hnlich sind. Die Neuronenschicht wirkt als topologische Merkmalskarte, wenn die Lage der am st\u00E4rksten erregten Neuronen in gesetzm\u00E4\u00DFiger und stetiger Weise mit wichtigen Signalmerkmalen korreliert ist."@de , "A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. Self-organizing maps are different than other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space. File:Synapse Self-Organizing Map. png A self-organizing map showing US Congress voting patterns visualized in Synapse. The first two boxes show clustering and distances while the remaining ones show the component planes. Red means a yes vote while blue means a no vote in the component planes (except the party component where red is Republican and blue is Democrat). This makes SOM useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map."@en , "Sie\u0107 Kohonena jest jednym z najbardziej znanych typ\u00F3w sieci neuronowych uczonej w trybie bez nauczyciela. Jest sieci\u0105 o bardzo prostej strukturze \u2013 posiada tylko dwie warstwy, a przep\u0142yw informacji w tej sieci jest \u015Bci\u015Ble jednokierunkowy. Mimo prostej budowy i nieskomplikowanych metod okre\u015Blaj\u0105cych spos\u00F3b jej funkcjonowania, mo\u017Cliwo\u015Bci aplikacyjne tego typu modelu s\u0105 olbrzymie."@pl ; rdfs:comment "\u0421\u0430\u043C\u043E\u043E\u0440\u0433\u0430\u043D\u0456\u0437\u0430\u0446\u0456\u0439\u043D\u0430 \u043A\u0430\u0440\u0442\u0430 \u041A\u043E\u0445\u043E\u043D\u0435\u043D\u0430 (\u0430\u043D\u0433\u043B. Self-organizing map \u2014 SOM) \u2014 \u043D\u0435\u0439\u0440\u043E\u043D\u043D\u0430 \u043C\u0435\u0440\u0435\u0436\u0430 \u0437 \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u044F\u043C \u0437\u0456 \u0437\u043C\u0430\u0433\u0430\u043D\u043D\u044F\u043C \u0431\u0435\u0437 \u0432\u0447\u0438\u0442\u0435\u043B\u044F, \u0449\u043E \u0432\u0438\u043A\u043E\u043D\u0443\u0454 \u0437\u0430\u0432\u0434\u0430\u043D\u043D\u044F \u043A\u043B\u0430\u0441\u0442\u0435\u0440\u0438\u0437\u0430\u0446\u0456\u0457."@uk , "Carte auto adaptative ou auto organisatrice est une classe de r\u00E9seau de neurones artificiels fond\u00E9e sur des m\u00E9thodes d'apprentissage non supervis\u00E9e. On la d\u00E9signe souvent par le terme anglais self organizing map (SOM), on encore carte de Kohonen du nom du statisticien ayant d\u00E9velopp\u00E9 le concept en 1984. Elles sont utilis\u00E9es pour cartographier un espace r\u00E9el, c'est-\u00E0-dire pour \u00E9tudier la r\u00E9partitions de donn\u00E9es dans un espace \u00E0 grande dimension."@fr , "Le self-organizing map (SOM) sono un particolare tipo di rete neurale artificiale. \u00C8 addestrata usando l'apprendimento non supervisionato per produrre una rappresentazione dei campioni di training in uno spazio a bassa dimensione preservando le propriet\u00E0 topologiche dello spazio degli ingressi. Questa propriet\u00E0 rende le SOM particolarmente utili per la visualizzazione di dati di dimensione elevata."@it , "A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. Self-organizing maps are different than other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space."@en , "\u0421\u0430\u043C\u043E\u043E\u0440\u0433\u0430\u043D\u0438\u0437\u0443\u044E\u0449\u0430\u044F\u0441\u044F \u043A\u0430\u0440\u0442\u0430 \u041A\u043E\u0445\u043E\u043D\u0435\u043D\u0430 (\u0430\u043D\u0433\u043B. Self-organizing map\u00A0\u2014 SOM)\u00A0\u2014 \u0441\u043E\u0440\u0435\u0432\u043D\u043E\u0432\u0430\u0442\u0435\u043B\u044C\u043D\u0430\u044F \u043D\u0435\u0439\u0440\u043E\u043D\u043D\u0430\u044F \u0441\u0435\u0442\u044C \u0441 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435\u043C \u0431\u0435\u0437 \u0443\u0447\u0438\u0442\u0435\u043B\u044F, \u0432\u044B\u043F\u043E\u043B\u043D\u044F\u044E\u0449\u0430\u044F \u0437\u0430\u0434\u0430\u0447\u0443 \u0432\u0438\u0437\u0443\u0430\u043B\u0438\u0437\u0430\u0446\u0438\u0438 \u0438 \u043A\u043B\u0430\u0441\u0442\u0435\u0440\u0438\u0437\u0430\u0446\u0438\u0438. \u0418\u0434\u0435\u044F \u0441\u0435\u0442\u0438 \u043F\u0440\u0435\u0434\u043B\u043E\u0436\u0435\u043D\u0430 \u0444\u0438\u043D\u0441\u043A\u0438\u043C \u0443\u0447\u0451\u043D\u044B\u043C \u0422. \u041A\u043E\u0445\u043E\u043D\u0435\u043D\u043E\u043C."@ru , "Itseorganisoiva kartta (engl. Self-Organizing Map, SOM) on akateemikko Teuvo Kohosen kehitt\u00E4m\u00E4 ohjaamattomaan oppimiseen perustuva neuroverkkomalli, jonka h\u00E4n julkaisi 1980-luvulla. Se on Kohosen kansainv\u00E4lisesti tunnetuin ty\u00F6. Mallista on k\u00E4ytetty my\u00F6s nime\u00E4 Kohonen map/network."@fi , "Los mapas autoorganizados o SOM (Self-Organizing Map), tambi\u00E9n llamados redes de Kohonen son un tipo de red neuronal no supervisada, competitiva, distribuida de forma regular en una rejilla de, normalmente, dos dimensiones, cuyo fin es descubrir la estructura subyacente de los datos introducidos en ella. A lo largo del entrenamiento de la red, los vectores de datos son introducidos en cada neurona y se comparan con el vector de peso caracter\u00EDstico de cada neurona."@es , "Als Selbstorganisierende Karten, Kohonenkarten oder Kohonennetze bezeichnet man eine Art von k\u00FCnstlichen neuronalen Netzen. Sie sind als un\u00FCberwachtes Lernverfahren ein leistungsf\u00E4higes Werkzeug des Data-Mining. Ihr Funktionsprinzip beruht auf der biologischen Erkenntnis, dass viele Strukturen im Gehirn eine lineare oder planare Topologie aufweisen. Die Signale des Eingangsraums, z. B. visuelle Reize, sind jedoch multidimensional."@de , ""@ja , "Sie\u0107 Kohonena jest jednym z najbardziej znanych typ\u00F3w sieci neuronowych uczonej w trybie bez nauczyciela. Jest sieci\u0105 o bardzo prostej strukturze \u2013 posiada tylko dwie warstwy, a przep\u0142yw informacji w tej sieci jest \u015Bci\u015Ble jednokierunkowy. Mimo prostej budowy i nieskomplikowanych metod okre\u015Blaj\u0105cych spos\u00F3b jej funkcjonowania, mo\u017Cliwo\u015Bci aplikacyjne tego typu modelu s\u0105 olbrzymie."@pl , "Een Kohonen-netwerk, zelf organiserend netwerk of self-Organizing Maps is een kunstmatig neuraal netwerk bedacht door de Finse professor Teuvo Kohonen."@nl ; foaf:depiction . @prefix skos: . @prefix ns14: . dbpedia:Self-organizing_map skos:subject ns14:Neural_networks , ns14:Machine_learning . @prefix ns15: . dbpedia:Self-organizing_map dbpprop:hasPhotoCollection ns15:Self-organizing_map . dbpedia:Kohonen_map dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Kohonen_maps dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Self-organising_maps dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Self-organizing_nets dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Self-Organizing_Map dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Self-organising_map dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Self-organizing_feature_map dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Sofm dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Kohonen dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Self_organizing_maps dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Self_Organising_Map dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Self_Organizing_Map dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Self_organising_map dbpprop:redirect dbpedia:Self-organizing_map . dbpedia:Self_organizing_map dbpprop:redirect dbpedia:Self-organizing_map . @prefix yago: . yago:Self-organizing_map owl:sameAs dbpedia:Self-organizing_map .