{"title":"学习的教训","authors":"John R. Smith","doi":"10.1109/MMUL.2013.39","DOIUrl":null,"url":null,"abstract":"Machine learning has become an indispensible tool for the multimedia community. Given large amounts of data, computers using machine learning are able to create rich representations and accomplish impressive discrimination tasks. Yet, the way machines learn is still differs significantly from how humans learn. EIC John R. Smith explains that the way forward is for the multimedia field to create appropriate lesson plans or more generally develop curriculum-based approaches to multimedia machine learning.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Lessons in Learning\",\"authors\":\"John R. Smith\",\"doi\":\"10.1109/MMUL.2013.39\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning has become an indispensible tool for the multimedia community. Given large amounts of data, computers using machine learning are able to create rich representations and accomplish impressive discrimination tasks. Yet, the way machines learn is still differs significantly from how humans learn. EIC John R. Smith explains that the way forward is for the multimedia field to create appropriate lesson plans or more generally develop curriculum-based approaches to multimedia machine learning.\",\"PeriodicalId\":290893,\"journal\":{\"name\":\"IEEE Multim.\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Multim.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMUL.2013.39\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Multim.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMUL.2013.39","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
摘要
机器学习已经成为多媒体社区不可或缺的工具。给定大量数据，使用机器学习的计算机能够创建丰富的表示并完成令人印象深刻的识别任务。然而，机器学习的方式仍然与人类的学习方式有很大不同。EIC John R. Smith解释说，多媒体领域的前进之路是创建合适的课程计划，或者更广泛地开发基于课程的多媒体机器学习方法。
Machine learning has become an indispensible tool for the multimedia community. Given large amounts of data, computers using machine learning are able to create rich representations and accomplish impressive discrimination tasks. Yet, the way machines learn is still differs significantly from how humans learn. EIC John R. Smith explains that the way forward is for the multimedia field to create appropriate lesson plans or more generally develop curriculum-based approaches to multimedia machine learning.
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