{"title":"分层投票专家:一种分层序列分割的无监督算法","authors":"Matthew Miller, A. Stoytchev","doi":"10.1109/DEVLRN.2008.4640827","DOIUrl":null,"url":null,"abstract":"This paper extends the voting experts (VE) algorithm for unsupervised segmentation of sequences to create the hierarchical voting experts (HVE) algorithm for unsupervised segmentation of hierarchically structured sequences. The paper evaluates the strengths and weaknesses of the HVE algorithm to identify its proper domain of application. The paper also shows how higher order models of the sequence data can be used to improve lower level segmentation accuracy.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Hierarchical voting experts: An unsupervised algorithm for hierarchical sequence segmentation\",\"authors\":\"Matthew Miller, A. Stoytchev\",\"doi\":\"10.1109/DEVLRN.2008.4640827\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper extends the voting experts (VE) algorithm for unsupervised segmentation of sequences to create the hierarchical voting experts (HVE) algorithm for unsupervised segmentation of hierarchically structured sequences. The paper evaluates the strengths and weaknesses of the HVE algorithm to identify its proper domain of application. The paper also shows how higher order models of the sequence data can be used to improve lower level segmentation accuracy.\",\"PeriodicalId\":366099,\"journal\":{\"name\":\"2008 7th IEEE International Conference on Development and Learning\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 7th IEEE International Conference on Development and Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DEVLRN.2008.4640827\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 7th IEEE International Conference on Development and Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DEVLRN.2008.4640827","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hierarchical voting experts: An unsupervised algorithm for hierarchical sequence segmentation
This paper extends the voting experts (VE) algorithm for unsupervised segmentation of sequences to create the hierarchical voting experts (HVE) algorithm for unsupervised segmentation of hierarchically structured sequences. The paper evaluates the strengths and weaknesses of the HVE algorithm to identify its proper domain of application. The paper also shows how higher order models of the sequence data can be used to improve lower level segmentation accuracy.
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