{"title":"有监督的信息论自组织映射的信息获取性能","authors":"R. Kamimura","doi":"10.1109/NaBIC.2014.6921870","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a new type of supervised multi-layered self-organizing map and examine to what extent information content in multi-layered networks can be increased. We have so far introduced the information-theoretic SOM in a single layer for increasing information content. However, we have found some cases where information content cannot be increased by single-layer networks. We used the multi-layered network and we found that mutual information tended to increase even for higher layers. The corresponding U-matrices showed clearer class structure even for higher layers. Then, we applied the method to the improvement of prediction performance. The prediction performance could be improved when the number of layers was appropriately chosen.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Information acquisition performance by supervised information-theoretic self-organizing maps\",\"authors\":\"R. Kamimura\",\"doi\":\"10.1109/NaBIC.2014.6921870\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a new type of supervised multi-layered self-organizing map and examine to what extent information content in multi-layered networks can be increased. We have so far introduced the information-theoretic SOM in a single layer for increasing information content. However, we have found some cases where information content cannot be increased by single-layer networks. We used the multi-layered network and we found that mutual information tended to increase even for higher layers. The corresponding U-matrices showed clearer class structure even for higher layers. Then, we applied the method to the improvement of prediction performance. The prediction performance could be improved when the number of layers was appropriately chosen.\",\"PeriodicalId\":209716,\"journal\":{\"name\":\"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NaBIC.2014.6921870\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NaBIC.2014.6921870","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Information acquisition performance by supervised information-theoretic self-organizing maps
In this paper, we propose a new type of supervised multi-layered self-organizing map and examine to what extent information content in multi-layered networks can be increased. We have so far introduced the information-theoretic SOM in a single layer for increasing information content. However, we have found some cases where information content cannot be increased by single-layer networks. We used the multi-layered network and we found that mutual information tended to increase even for higher layers. The corresponding U-matrices showed clearer class structure even for higher layers. Then, we applied the method to the improvement of prediction performance. The prediction performance could be improved when the number of layers was appropriately chosen.