Explanation-Based Neural Network Learning: A Lifelong Learning Approach

di Sebastian Thrun (Autore)

Springer-Verlag New York Inc., 2011

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.

• Autore: Sebastian Thrun
• Editore: Springer-Verlag New York Inc.
• Collana: The Springer International Series in Engineering and Computer Science
• Anno: 2011
• Rilegatura: Paperback / softback
• Pagine: 264 p.
• Dimensioni: 235 x 155 mm
• EAN: 9781461285977

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