Explanation-Based Neural Network Learning : A Lifelong Learning Approach

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.