Transfer learning deals with how machine learning and artificial intelligence systems can quickly adapt to new tasks and environments. This in-depth tutorial for students, researchers, and developers covers foundations, plus applications such as text mining, inference on social networks, recommendation, multimedia, and cyber-physical systems.
Transfer learning deals with how machine learning and artificial intelligence systems can quickly adapt to new tasks and environments. This in-depth tutorial for students, researchers, and developers covers foundations, plus applications such as text mining, inference on social networks, recommendation, multimedia, and cyber-physical systems.
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.
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.
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