无监督序列处理的联想记忆模型

PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005. Pub Date : 2005-10-17 DOI:10.1109/PACRIM.2005.1517268

S. Pantazi, J. Moehr

{"title":"无监督序列处理的联想记忆模型","authors":"S. Pantazi, J. Moehr","doi":"10.1109/PACRIM.2005.1517268","DOIUrl":null,"url":null,"abstract":"We introduce the design principles and present formally the building block of an associative memory model capable of unsupervised sequence processing: the constrained partially ordered set. We then use the model in a series of experiments, presented in increasing order of complexity and conclude that it demonstrates interesting information processing capabilities which warrant future development.","PeriodicalId":346880,"journal":{"name":"PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An associative memory model for unsupervised sequence processing\",\"authors\":\"S. Pantazi, J. Moehr\",\"doi\":\"10.1109/PACRIM.2005.1517268\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce the design principles and present formally the building block of an associative memory model capable of unsupervised sequence processing: the constrained partially ordered set. We then use the model in a series of experiments, presented in increasing order of complexity and conclude that it demonstrates interesting information processing capabilities which warrant future development.\",\"PeriodicalId\":346880,\"journal\":{\"name\":\"PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005.\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PACRIM.2005.1517268\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACRIM.2005.1517268","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

我们介绍了设计原则，并正式提出了一种能够进行无监督序列处理的联想记忆模型的构建块:约束部分有序集。然后，我们在一系列实验中使用该模型，以增加复杂性的顺序呈现，并得出结论，它展示了有趣的信息处理能力，保证了未来的发展。

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

An associative memory model for unsupervised sequence processing

We introduce the design principles and present formally the building block of an associative memory model capable of unsupervised sequence processing: the constrained partially ordered set. We then use the model in a series of experiments, presented in increasing order of complexity and conclude that it demonstrates interesting information processing capabilities which warrant future development.

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

PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005.

自引率

0.00%

发文量