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020 _a9783031015816
_9978-3-031-01581-6
024 7 _a10.1007/978-3-031-01581-6
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aChen, Zhiyuan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982060
245 1 0 _aLifelong Machine Learning, Second Edition
_h[electronic resource] /
_cby Zhiyuan Chen, Bing Liu.
250 _a2nd ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIX, 187 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aPreface -- Acknowledgments -- Introduction -- Related Learning Paradigms -- Lifelong Supervised Learning -- Continual Learning and Catastrophic Forgetting -- Open-World Learning -- Lifelong Topic Modeling -- Lifelong Information Extraction -- Continuous Knowledge Learning in Chatbots -- Lifelong Reinforcement Learning -- Conclusion and Future Directions -- Bibliography -- Authors' Biographies.
520 _aLifelong 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 neural networks-which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning-most notably, multi-task learning, transfer learning, and meta-learning-because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_982061
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aLiu, Bing.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982062
710 2 _aSpringerLink (Online service)
_982063
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000263
776 0 8 _iPrinted edition:
_z9783031004537
776 0 8 _iPrinted edition:
_z9783031027093
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_982064
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01581-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c85291
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