MARC状态：审校 文献类型：西文图书 浏览次数：18
- 题名/责任者:
- Transfer learning / Qiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan.
- 出版发行项:
- Cambridge : Cambridge University Press, 2020.
- ISBN:
- 9781107016903
- 载体形态项:
- 393 pages : 24 cm
- 个人责任者:
- Yang, Qiang, 1961- author.
- 附加个人名称:
- Zhang, Yu (Professor of computer science), 1982- author.
- 附加个人名称:
- Dai, Wenyuan, 1983- author.
- 附加个人名称:
- Pan, Sinno Jialin, 1980- author.
- 论题主题:
- Machine learning.
- 论题主题:
- Artificial intelligence.
- 中图法分类号:
- TP1
- 书目附注:
- Includes bibliographical references and index.
- 内容附注:
- 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.
- 摘要附注:
- "This book is about the foundations, methods, techniques and applications of transfer learning. Transfer learning deals with how learning systems can quickly adapt themselves to new situations, new tasks and new environments. Transfer learning is a particularly important area of machine learning, which we can understand from several angles. First, the ability to learn from small data seems to be a particularly strong aspect of human intelligence. For example, we observe that babies learn from only a few examples and can quickly and effectively generalize from the few examples to concepts. This ability to learn from small data can be partly explained by the ability of humans to leverage and adapt the previous experience and pre-trained models to help solve future target problems. Adaptation is an innate ability of intelligent beings and artificially intelligent agents should certainly be endowed with transfer-learning ability"--
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