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Titlebook: Lifelong Machine Learning, Second Edition; Zhiyuan Chen,Bing Liu Book 2018Latest edition Springer Nature Switzerland AG 2018

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发表于 2025-3-21 18:54:09 | 显示全部楼层 |阅读模式
书目名称Lifelong Machine Learning, Second Edition
编辑Zhiyuan Chen,Bing Liu
视频video
丛书名称Synthesis Lectures on Artificial Intelligence and Machine Learning
图书封面Titlebook: Lifelong Machine Learning, Second Edition;  Zhiyuan Chen,Bing Liu Book 2018Latest edition Springer Nature Switzerland AG 2018
描述.Lifelong Machine Learning, Second Edition. is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent...Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep ne
出版日期Book 2018Latest edition
版次2
doihttps://doi.org/10.1007/978-3-031-01581-6
isbn_softcover978-3-031-00453-7
isbn_ebook978-3-031-01581-6Series ISSN 1939-4608 Series E-ISSN 1939-4616
issn_series 1939-4608
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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Lifelong Information Extraction, extracted information earlier can be used to help extract more information later with higher quality [Carlson et al., 2010a, Liu et al., 2016, Shu et al., 2017b]. These all match the goal of LL. In this case, the knowledge base (KB) of LL often stores the extracted information and some other forms of useful knowledge.
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Conclusion and Future Directions,d practitioners about the differences between these learning paradigms, which is not surprising as they are indeed similar and related. Hopefully, our new definition of LL in Section 1.4 and subsequent discussions in Chapter 2 help clarify the differences and resolve the confusions.
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1939-4608 ting past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended applicatio
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Book 2018Latest editionknowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It make
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