Explanation-Based Neural Network Learning: A Lifelong Learning Approach (The Springer International Series in Engineering and Computer Science, 357)

Thrun, Sebastian

ISBN 10: 0792397169 / ISBN 13: 9780792397168

Editore: Springer, 1996

Riguardo questo articolo

In shrink wrap. Looks like an interesting title! Codice articolo Q-0792397169

Dati bibliografici

Titolo: Explanation-Based Neural Network Learning: A...

Casa editrice: Springer

Data di pubblicazione: 1996

Legatura: Hardcover

Condizione: New

Descrizione articolo

Riassunto:

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.

Contenuti:

Preface. 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.