{"title":"利用神经网络挖掘台风知识","authors":"Zhi-Hua Zhou, Shifu Chen, Zhaoqian Chen","doi":"10.1109/TAI.1999.809809","DOIUrl":null,"url":null,"abstract":"Neural network technology has not been fully utilised in data mining. There are two reasons for this. Firstly, most neural algorithms need long-term training, and cannot perform incremental learning. Secondly, the knowledge learned by neural networks is concealed within a large quantity of connections. We have develop a neural network method to mine typhoon knowledge which overcomes those two obstacles. Experimental results show that our method shows considerable promise.","PeriodicalId":194023,"journal":{"name":"Proceedings 11th International Conference on Tools with Artificial Intelligence","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Mining typhoon knowledge with neural networks\",\"authors\":\"Zhi-Hua Zhou, Shifu Chen, Zhaoqian Chen\",\"doi\":\"10.1109/TAI.1999.809809\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Neural network technology has not been fully utilised in data mining. There are two reasons for this. Firstly, most neural algorithms need long-term training, and cannot perform incremental learning. Secondly, the knowledge learned by neural networks is concealed within a large quantity of connections. We have develop a neural network method to mine typhoon knowledge which overcomes those two obstacles. Experimental results show that our method shows considerable promise.\",\"PeriodicalId\":194023,\"journal\":{\"name\":\"Proceedings 11th International Conference on Tools with Artificial Intelligence\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 11th International Conference on Tools with Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAI.1999.809809\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 11th International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1999.809809","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural network technology has not been fully utilised in data mining. There are two reasons for this. Firstly, most neural algorithms need long-term training, and cannot perform incremental learning. Secondly, the knowledge learned by neural networks is concealed within a large quantity of connections. We have develop a neural network method to mine typhoon knowledge which overcomes those two obstacles. Experimental results show that our method shows considerable promise.