Titlebook: Lifelong Machine Learning; Zhiyuan Chen,Bing Liu Book 2017 Springer Nature Switzerland AG 2017

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Book 2017ned 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.: g

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Introduction, social sciences. Practical applications are even more widespread. It is safe to say that without effective ML algorithms, many industries would not have flourished, e.g., Internet commerce and Web search.

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Book 2017o 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 le

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1939-4608 tistical 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 le978-3-031-01575-5Series ISSN 1939-4608 Series E-ISSN 1939-4616