Lifelong learning addresses situations in which a learner faces a
series of different learning tasks providing the opportunity for
synergy among them. Explanation-based neural network learning
(EBNN) is a machine learning algorithm that transfers knowledge
across multiple learning tasks. When faced with a new learning
task, EBNN exploits domain knowledge accumulated in previous
learning tasks to guide generalization in the new one. As a result,
EBNN generalizes more accurately from less data than comparable
methods. Explanation-Based Neural Network Learning: A Lifelong
Learning Approach describes the basic EBNN paradigm and
investigates it in the context of supervised learning,
reinforcement learning, robotics, and chess. The paradigm of
lifelong learning - using earlier learned knowledge to improve
subsequent learning - is a promising direction for a new generation
of machine learning algorithms. Given the need for more accurate
learning methods, it is difficult to imagine a future for machine
learning that does not include this paradigm.' From the Foreword by
Tom M. Mitchell.
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