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Lifelong Machine Learning
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  • Title Lifelong Machine Learning
  • Author(s) Zhiyuan Chen (Author), Bing Liu (Author)
  • Publisher: Morgan & Claypool; 1st Ed (2016); 2nd Ed (2018); eBook (Final Draft)
  • Hardcover 146 pages
  • eBook PDF (145 pages)
  • Language: English
  • ISBN-10: 1681733048
  • ISBN-13: 978-1681733043
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Book Description

This book is an introduction to an advanced Machine Learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, 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 that is then used in its intended application.

It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent.

About the Authors
  • Zhiyuan Chen is a Staff Software Engineer & Tech Lead Manager at Google. He 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.
  • Bing Liu is a Distinguished Professor of Computer Science at the University of Illinois at Chicago.
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