Titlebook: Lifelong Machine Learning, Second Edition; Zhiyuan Chen,Bing Liu Book 2018Latest edition Springer Nature Switzerland AG 2018

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Introduction,g brought ML to a new height. ML algorithms have been applied in almost all areas of computer science, natural science, engineering, social sciences, and beyond. Practical applications are even more widespread. Without effective ML algorithms, many industries would not have existed or flourished, e.

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Lifelong Supervised Learning,s tasks is useful and how such sharing makes LSL work. The example is about product review sentiment classification. The task is to build a classifier to classify a product review as expressing a positive or negative opinion. In the classic setting, we first label a large number of positive opinion

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Continual Learning and Catastrophic Forgetting, it is well-known that deep neural networks (DNNs) have achieved state-of-the-art performances in many machine learning (ML) tasks, the standard multi-layer perceptron (MLP) architecture and DNNs suffer from . [McCloskey and Cohen, 1989] which makes it difficult for continual learning. The problem i

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Continuous Knowledge Learning in Chatbots,nt is a key capability of human beings. One can only learn so much by being told or supervised because the world is simply too complex to be completely learned this way. In fact, we humans probably learn a great deal of our knowledge through interactions with other humans and the environment around

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Lifelong Reinforcement Learning, environment [Kaelbling et al., 1996, Sutton and Barto, 1998]. In each interaction step, the agent receives input on the current state of the environment. It chooses an action from a set of possible actions. The action changes the state of the environment. Then, the agent gets the value of this stat

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