{"title":"序列数据的最优分割","authors":"J. Kohlmorgen","doi":"10.1109/NNSP.2003.1318044","DOIUrl":null,"url":null,"abstract":"We present an algorithm that efficiently computes optimal partitions of sequential data into 1 to N segments and propose a method to determine the most salient segmentation among them. As a by-product, we obtain a regularization parameter that can be used to compute such salient segmentations - also on new data sets - even more efficiently.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"On optimal segmentation of sequential data\",\"authors\":\"J. Kohlmorgen\",\"doi\":\"10.1109/NNSP.2003.1318044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present an algorithm that efficiently computes optimal partitions of sequential data into 1 to N segments and propose a method to determine the most salient segmentation among them. As a by-product, we obtain a regularization parameter that can be used to compute such salient segmentations - also on new data sets - even more efficiently.\",\"PeriodicalId\":315958,\"journal\":{\"name\":\"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.2003.1318044\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.2003.1318044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present an algorithm that efficiently computes optimal partitions of sequential data into 1 to N segments and propose a method to determine the most salient segmentation among them. As a by-product, we obtain a regularization parameter that can be used to compute such salient segmentations - also on new data sets - even more efficiently.