贝叶斯在线学习:具有rao - blackwell化的顺序蒙特卡罗

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI:10.1109/NNSP.2002.1030021

K. Yosui, T. Kurihara, K. Wada, T. Souma, Takashi Matsumoto

{"title":"贝叶斯在线学习:具有rao - blackwell化的顺序蒙特卡罗","authors":"K. Yosui, T. Kurihara, K. Wada, T. Souma, Takashi Matsumoto","doi":"10.1109/NNSP.2002.1030021","DOIUrl":null,"url":null,"abstract":"This paper proposes a Rao-Blackwellised sequential Monte Carlo (RBSMC) scheme for on-line learning with feedforward neural nets. The proposed algorithm is tested against an example and the performance is compared with those of the conventional sequential Monte Carlo as well as the extended Kalman filter (EKF). The proposed scheme outperforms those conventional algorithms.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"42 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Bayesian on-line learning: a sequential Monte Carlo with Rao-Blackwellization\",\"authors\":\"K. Yosui, T. Kurihara, K. Wada, T. Souma, Takashi Matsumoto\",\"doi\":\"10.1109/NNSP.2002.1030021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a Rao-Blackwellised sequential Monte Carlo (RBSMC) scheme for on-line learning with feedforward neural nets. The proposed algorithm is tested against an example and the performance is compared with those of the conventional sequential Monte Carlo as well as the extended Kalman filter (EKF). The proposed scheme outperforms those conventional algorithms.\",\"PeriodicalId\":117945,\"journal\":{\"name\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"volume\":\"42 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"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.2002.1030021\",\"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.2002.1030021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 10

摘要

提出了一种用于前馈神经网络在线学习的rao - blackwell化序贯蒙特卡罗(RBSMC)方案。通过实例验证了该算法的性能，并与传统的时序蒙特卡罗滤波和扩展卡尔曼滤波(EKF)进行了比较。该方案优于传统算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Bayesian on-line learning: a sequential Monte Carlo with Rao-Blackwellization

This paper proposes a Rao-Blackwellised sequential Monte Carlo (RBSMC) scheme for on-line learning with feedforward neural nets. The proposed algorithm is tested against an example and the performance is compared with those of the conventional sequential Monte Carlo as well as the extended Kalman filter (EKF). The proposed scheme outperforms those conventional algorithms.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing

自引率

0.00%

发文量