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008			220601s2018 sz | s |||| 0|eng d
020			_a9783031015816
024	7		_a10.1007/978-3-031-01581-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2018 EB
100	1		_aChen, Zhiyuan _c(Computer scientist) _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687859
245	1	0	_aLifelong Machine Learning _cby Zhiyuan Chen, Bing Liu
250			_a2nd edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIX, 187 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aLiu, Bing, _d1963- _4aut _eautor _4http://id.loc.gov/vocabulary/relators/aut _954426
776	0	8	_iPrinted edition: _z9783031000263
776	0	8	_iPrinted edition: _z9783031004537
776	0	8	_iPrinted edition: _z9783031027093
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01581-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI