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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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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.
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Explanation-Based Neural Network Learning: A Lifelong Learning Approach The Kluwer International Series in Engineering and Computer Science
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Kluwer Academic Publishers, 1996
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Explanation-Based Neural Network Learning: A Lifelong Learning Approach (The Springer International Series in Engineering and Computer Science, 357)
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Hardcover. Condizione: new. Hardcover. 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. This book describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics and chess. Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780792397168
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Explanation-Based Neural Network Learning : A Lifelong Learning Approach
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Explanation-Based Neural Network Learning
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Buch. Condizione: Neu. 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. Codice articolo 9780792397168
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