Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.
Spis treści
Preface.- Acknowledgments.- Introduction.- Related Learning Paradigms.- Lifelong Supervised Learning.- Lifelong Unsupervised Learning.- Lifelong Semi-supervised Learning for Information Extraction.- Lifelong Reinforcement Learning.- Conclusion and Future Directions.- Bibliography.- Authors’ Biographies.
O autorze
Zhiyuan Chen completed his Ph.D., titled ”Lifelong Machine Learning for Topic Modeling and Classification, ” at the University of Illinois at Chicago under the direction of Professor Bing Liu. He joined Google in 2016. His research interests include machine learning, natural language processing, text mining, data mining, and auction algorithms. He has proposed several lifelong learning algorithms to automatically mine information from text documents, and published more than 15 full research papers in premier conferences such as KDD, ICML, ACL, WWW, IJCAI, and AAAI. He has also given three tutorials about lifelong machine learning at IJCAI-2015, KDD-2016, and EMNLP-2016. He has served as a PC member for many prestigious natural language processing, data mining, AI, and Web research conferences. In recognition of his academic contributions, he was awarded Fifty For The Future Award from the Illinois Technology Foundation in 2015.
Bing Liu is a Distinguished Professor of Computer Science at the University of Illinois at Chicago. He received his Ph.D. in Artificial Intelligence from the University of Edinburgh. His research interests include lifelong machine learning, sentiment analysis and opinion mining, data mining, machine learning, and natural language processing. He has published extensively in top conferences and journals in these areas. Two of his papers have received 10-year Test-of-Time awards from KDD, the premier conference of data mining and data science. He has also authored three books: one on Web data mining and two on sentiment analysis. Some of his work has been widely reported in the popular press, including a front-page article in the New York Times. On professional services, he served as the Chair of ACM SIGKDD from 2013-2017, as program chair of many leading data mining related conferences, including KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, as associate editor of many leading journals such as TKDE, TKDD, TWEB, and DMKD, and as area chair or senior PC member of numerous natural language processing, AI, Web research, and data mining conferences. He is a Fellow of the ACM, AAAI, and IEEE.
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