Qiang Yang

Transfer Learning (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 13. Februar 2020
• Englisch
• ISBN-13: 9781108860086
• Artikelnr.: 66182122

Autorenporträt

Qiang Yang is the Head of AI at WeBank and a Chair Professor of Computer Science and Engineering at Hong Kong University of Science and Technology. He is a fellow of the Association for Computing Machinery (ACM), Association for the Advancement of Artificial Intelligence (AAAI), Institute of Electrical and Electronics Engineers (IEEE), International Association for Pattern Recognition (IAPR) and American Association for the Advancement of Science (AAAS), and has served on the AAAI Executive Council and as President of IJCAI. Awards include the 2004/2005 ACM KDDCUP Championship, the ACM SIGKDD Distinguished Service Award, and AAAI Innovative AI Applications Award. His books include Intelligent Planning (1997), Crafting Your Research Future (2012) and Constraint-based Design Recovery for Software Engineering (1997), and he is Founding EIC of the IEEE Transactions on Intelligent Systems and Technology and on Big Data.

Inhaltsangabe

1. Introduction
2. Instance-based transfer learning
3. Feature-based transfer learning
4. Model-based transfer learning
5. Relation-based transfer learning
6. Heterogeneous transfer learning
7. Adversarial transfer learning
8. Transfer learning in reinforcement learning
9 Multi-task learning
10. Transfer learning theory
11. Transitive transfer learning
12. AutoTL: learning to transfer automatically
13. Few-shot learning
14. Lifelong machine learning
15. Privacy-preserving transfer learning
16. Transfer learning in computer vision
17. Transfer learning in natural language processing
18. Transfer learning in dialogue systems
19. Transfer learning in recommender systems
20. Transfer learning in bioinformatics
21. Transfer learning in activity recognition
22. Transfer learning in urban computing
23. Concluding remarks.