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Titlebook: Explanation-Based Neural Network Learning; A Lifelong Learning Sebastian Thrun Book 1996 Kluwer Academic Publishers 1996 algorithms.artifi

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书目名称Explanation-Based Neural Network Learning
副标题A Lifelong Learning
编辑Sebastian Thrun
视频video
丛书名称The Springer International Series in Engineering and Computer Science
图书封面Titlebook: Explanation-Based Neural Network Learning; A Lifelong Learning  Sebastian Thrun Book 1996 Kluwer Academic Publishers 1996 algorithms.artifi
描述Lifelong learning addresses situations in which a learner facesa series of different learning tasks providing the opportunity forsynergy among them. Explanation-based neural network learning (EBNN)is a machine learning algorithm that transfers knowledge acrossmultiple learning tasks. When faced with a new learning task, EBNNexploits domain knowledge accumulated in previous learning tasks toguide generalization in the new one. As a result, EBNN generalizesmore accurately from less data than comparable methods..Explanation-Based Neural Network Learning: A Lifelong Learning..Approach. describes the basic EBNN paradigm and investigates itin the context of supervised learning, reinforcement learning,robotics, and chess. ..`.The paradigm of lifelong learning - using earlierlearned knowledge to improve subsequent learning - is apromising direction for a new generation of machine learningalgorithms. Given the need for more accurate learning methods, it isdifficult to imagine a future for machine learning that does notinclude this paradigm..‘. .From the Foreword by Tom M. Mitchell.
出版日期Book 1996
关键词algorithms; artificial neural network; knowledge; learning; lifelong learning; machine learning; neural ne
版次1
doihttps://doi.org/10.1007/978-1-4613-1381-6
isbn_softcover978-1-4612-8597-7
isbn_ebook978-1-4613-1381-6Series ISSN 0893-3405
issn_series 0893-3405
copyrightKluwer Academic Publishers 1996
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Empirical Results,Armed with an algorithm for learning from delayed reward, we are now ready to apply EBNN in the context of lifelong control learning. This chapter deals with the application of .-Learning and EBNN in the context of robot control and chess. The key questions underlying this research are:
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978-1-4612-8597-7Kluwer Academic Publishers 1996
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Zur Geschichte der Sozialhygiene,ng to learn, humans often learn and generalize successfully from a remarkably small number of training examples. Sometimes a single learning example suffices to generalize reliably in other, similar situations. For example, a single view of a person often suffices to recognize this person reliably e
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Berufsmorbidität und -mortalitäthes the meta-level learning problem by learning a .. This domain theory is domain-specific. It characterizes, for example, the relevance of individual features, their cross-dependencies, or certain invariant properties of the domain that apply to all learning tasks within the domain. Obviously, when
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A. Gottstein,A. Schlossmann,R. Volkcontext: the learner is assumed to face supervised learning problems of the same type and, moreover, these learning problems must be related by some domain-specific properties (casted as invariances) that are unknown in the beginning of lifelong learning but can be learned. Central to the learning a
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