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008			221028s2017 sz | s |||| 0|eng d
020			_a9783031015755
024	7		_a10.1007/978-3-031-01575-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2017 EB
100	1		_aChaudhri, Zhiyuan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686941
245	1	0	_aLifelong Machine Learning _cby Zhiyuan Chaudhri, Bing Liu
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (IV, 145 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 -- Lifelong Unsupervised Learning -- Lifelong Semi-supervised Learning for Information Extraction -- Lifelong Reinforcement Learning -- Conclusion and Future Directions -- Bibliography -- Authors' Biographies.
520			_aLifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, 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. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aLiu, Bing, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _954426 _d1963-
776	0	8	_iPrinted edition: _z9783031004476
776	0	8	_iPrinted edition: _z9783031027031
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01575-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI