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Titlebook: Learning and Generalisation; With Applications to M. Vidyasagar Book 2003Latest edition Springer-Verlag London 2003 Computer.Control Theory

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發(fā)表于 2025-3-21 19:10:25 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Learning and Generalisation
副標題With Applications to
編輯M. Vidyasagar
視頻videohttp://file.papertrans.cn/583/582880/582880.mp4
概述Comprehensive; this book covers all aspects of learning theory and its applications. Other books have a narrower focus.It contains applications not only to neural networks but also to control systems.
叢書名稱Communications and Control Engineering
圖書封面Titlebook: Learning and Generalisation; With Applications to M. Vidyasagar Book 2003Latest edition Springer-Verlag London 2003 Computer.Control Theory
描述.Learning and Generalization. provides a formal mathematical theory addressing intuitive questions of the type: ..? How does a machine learn a concept on the basis of examples?..? How can a neural network, after training, correctly predict the outcome of a previously unseen input?..? How much training is required to achieve a given level of accuracy in the prediction?..? How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite time?..The second edition covers new areas including:..? support vector machines;..? fat-shattering dimensions and applications to neural network learning;..? learning with dependent samples generated by a beta-mixing process;..? connections between system identification and learning theory;..? probabilistic solution of ‘intractable problems‘ in robust control and matrix theory using randomized algorithms...It also contains solutions to some of the open problems posed in the first edition, while adding new open problems. .
出版日期Book 2003Latest edition
關鍵詞Computer; Control Theory; Robust Control; Stochastic Processes; Support Vector Machine; Support Vector Ma
版次2
doihttps://doi.org/10.1007/978-1-4471-3748-1
isbn_softcover978-1-84996-867-6
isbn_ebook978-1-4471-3748-1Series ISSN 0178-5354 Series E-ISSN 2197-7119
issn_series 0178-5354
copyrightSpringer-Verlag London 2003
The information of publication is updating

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Book 2003Latest editionen system identification and learning theory;..? probabilistic solution of ‘intractable problems‘ in robust control and matrix theory using randomized algorithms...It also contains solutions to some of the open problems posed in the first edition, while adding new open problems. .
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發(fā)表于 2025-3-22 09:48:28 | 只看該作者
Distribution-Free Learning,nsion or the P-dimension. Moreover, we will see in Chapter 8 that, under suitable conditions, a little prior knowledge about the probability does not really help, in the following sense: Learning when there is so-called nonparametric uncertainty about the underlying probability measure is as difficu
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Applications to Neural Networks,ficiently large number of input-output pairs, it can then correctly predict all future input-output pairs, even for those inputs that the network has not seen previously. In the absence of the generalization ability, there is no reason to use a neural network merely to reproduce . input-output pairs
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發(fā)表于 2025-3-22 19:13:47 | 只看該作者
M. Vidyasagar PhD. Studies have shown the efficacy of IVC filters for reducing the risk of recurrent symptomatic PE but have also revealed an increased risk for DVT and clot propagation. Additionally, there has been increasing recognition of device-related complications, including filter migration, fracture, and pen
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M. Vidyasagar PhDieval includes the use of endovascular snares; however alternative methods such as endobronchial forceps or laser sheath ablation may be needed in complicated cases. Studies have shown the efficacy of IVC filters for reducing the risk of recurrent symptomatic PE but have also revealed an increased r
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