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...

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Bibliographische Detailangaben
Beteilige Person: Thrun, Sebastian (VerfasserIn)
Format: Elektronisch E-Book
Sprache:Englisch
Veröffentlicht: Boston, MA Springer US 1996
Schriftenreihe:The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems 357
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Links:https://doi.org/10.1007/978-1-4613-1381-6
https://doi.org/10.1007/978-1-4613-1381-6
Zusammenfassung: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
Umfang:1 Online-Ressource (XVI, 264 p)
ISBN:9781461313816
DOI:10.1007/978-1-4613-1381-6