Transfer learning [electronic resource] / Qiang Yang ... [et al.].
- 作者： Yang, Qiang, 1961-
- 出版： Cambridge : Cambridge University Press 2020.
- 主題： Machine learning. , Artificial intelligence.
- ISBN： 9781139061773 (electronic bk.) 、 9781107016903 (hardback)
- URL：
電子書(校內)
電子書(校外)
- 一般註：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. 113年度臺灣學術電子書暨資料庫聯盟採購
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讀者標籤：
- 系統號： 000312101 | 機讀編目格式
館藏資訊
摘要註
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.