000			03056nam a2200373 i 4500
001			CR9781139061773
003			UkCbUP
005			20240301142640.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			110418s2020||||enk o ||1 0|eng|d
020			_a9781139061773 (ebook)
020			_z9781107016903 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQ325.5 _b.Y366 2020
082	0	0	_a006.3/1 _223
100	1		_aYang, Qiang, _d1961- _eauthor.
245	1	0	_aTransfer learning / _cQiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan.
264		1	_aCambridge : _bCambridge University Press, _c2020.
300			_a1 online resource (xi, 379 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 29 Jan 2020).
505	0		_aInstance-based transfer learning -- Feature-based transfer learning -- Model-based transfer learning -- Relation-based transfer learning -- Heterogeneous transfer learning -- Adversarial transfer learning -- Transfer learning in reinforcement learning -- Multi-task learning -- Transfer learning theory -- Transitive transfer learning -- AutoTL : learning to transfer automatically -- Few-shot learning -- Lifelong machine learning -- Privacy-preserving transfer learning -- Transfer learning in computer vision -- Transfer learning in natural language processing -- Transfer learning in dialogue systems -- Transfer learning in recommender systems -- Transfer learning in bioinformatics -- Transfer learning in activity recognition -- Transfer learning in urban computing.
520			_aTransfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.
650		0	_aMachine learning.
650		0	_aArtificial intelligence.
700	1		_aZhang, Yu _c(Professor of computer science), _d1982- _eauthor.
700	1		_aDai, Wenyuan, _d1983- _eauthor.
700	1		_aPan, Sinno Jialin, _d1980- _eauthor.
776	0	8	_iPrint version: _z9781107016903
856	4	0	_uhttps://doi.org/10.1017/9781139061773
999			_c9992 _d9992