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.
|Series:||The Springer International Series in Engineering and Computer Science , #357|
|Edition description:||Softcover reprint of the original 1st ed. 1996|
|Product dimensions:||6.10(w) x 9.25(h) x 0.02(d)|
Table of ContentsPreface. 1. Introduction. 2. Explanation-Based Neural Network Learning. 3. The Invariance Approach. 4. Reinforcement Learning. 5. Empirical Results. 6. Discussion. A. An Algorithm for Approximating Values and Slopes with Artificial Neural Networks. B. Proofs of the Theorems. C. Example Chess Games. References. List of Symbols. Index.