Explanation-Based Neural Network Learning

Sebastian Thrun

ISBN 10: 0792397169 / ISBN 13: 9780792397168

Verlag: Springer US Apr 1996, 1996

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 292 pp. Englisch.

Bestandsnummer des Verkäufers 9780792397168

Bibliografische Details

• Titel: Explanation-Based Neural Network Learning
• Verlag: Springer US Apr 1996
• Erscheinungsjahr: 1996
• Sprache: Englisch
• ISBN-10: 0792397169
• ISBN-13: 9780792397168
• Einband: Buch
• Zustand: Neu
• Abmessungen: 241B x 160 H x 21 T Millimeter Breite: 241 mm, Höhe: 160 mm, Tiefe: 21 mm
• Gewicht: 606 Gramm

Über diesen Titel

Inhaltsangabe

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.