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Transfer learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Transfer learningQiang Yang ... [et al.].
作者:	Yang, Qiang,
出版者:	Cambridge :Cambridge University Press,2020.
面頁冊數:	xi, 379 p. :ill., digital ;23 cm.
附註:	Title from publisher's bibliographic system (viewed on 29 Jan 2020).
標題:	Machine learning.
電子資源:	https://doi.org/10.1017/9781139061773
ISBN:	9781139061773

Transfer learning
Yang, Qiang,1961-

Transfer learning[electronic resource] /Qiang Yang ... [et al.]. - Cambridge :Cambridge University Press,2020. - xi, 379 p. :ill., digital ;23 cm.

Title from publisher's bibliographic system (viewed on 29 Jan 2020).

Instance-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.

Transfer 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.

ISBN: 9781139061773Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .Y366 2020

Dewey Class. No.: 006.31

Transfer learning
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500 $a Title from publisher's bibliographic system (viewed on 29 Jan 2020).
505 0 $a Instance-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 $a Transfer 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.
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650 0 $a Artificial intelligence. $3 194058
856 4 0 $u https://doi.org/10.1017/9781139061773