{"title":"生物声学时间序列分类决策模板","authors":"C. Dietrich, G. Palm, F. Schwenker","doi":"10.1109/NNSP.2002.1030027","DOIUrl":null,"url":null,"abstract":"The classification of time series is topic of this paper. In particular we discuss the combination of multiple classifier outputs with decision templates. The decision templates are calculated over a set of feature vectors which are extracted in local time windows. To learn characteristic classifier outputs of time series a set of decision templates is determined for the individual classes. We present algorithms to calculate multiple decision templates, and demonstrate the behaviour of this new approach on a real world data set from the field of bioacoustics.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"53","resultStr":"{\"title\":\"Decision templates for the classification of bioacoustic time series\",\"authors\":\"C. Dietrich, G. Palm, F. Schwenker\",\"doi\":\"10.1109/NNSP.2002.1030027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The classification of time series is topic of this paper. In particular we discuss the combination of multiple classifier outputs with decision templates. The decision templates are calculated over a set of feature vectors which are extracted in local time windows. To learn characteristic classifier outputs of time series a set of decision templates is determined for the individual classes. We present algorithms to calculate multiple decision templates, and demonstrate the behaviour of this new approach on a real world data set from the field of bioacoustics.\",\"PeriodicalId\":117945,\"journal\":{\"name\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"53\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.2002.1030027\",\"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 of the 12th IEEE Workshop on Neural Networks for Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.2002.1030027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Decision templates for the classification of bioacoustic time series
The classification of time series is topic of this paper. In particular we discuss the combination of multiple classifier outputs with decision templates. The decision templates are calculated over a set of feature vectors which are extracted in local time windows. To learn characteristic classifier outputs of time series a set of decision templates is determined for the individual classes. We present algorithms to calculate multiple decision templates, and demonstrate the behaviour of this new approach on a real world data set from the field of bioacoustics.
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