| 书目名称 | Learning and Generalisation |
| 副标题 | With Applications to |
| 编辑 | M. Vidyasagar |
| 视频video | |
| 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. |
| 丛书名称 | Communications and Control Engineering |
| 图书封面 |  |
| 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 |
| 关键词 | Computer; Control Theory; Robust Control; Stochastic Processes; Support Vector Machine; Support Vector Ma |
| 版次 | 2 |
| doi | https://doi.org/10.1007/978-1-4471-3748-1 |
| isbn_softcover | 978-1-84996-867-6 |
| isbn_ebook | 978-1-4471-3748-1Series ISSN 0178-5354 Series E-ISSN 2197-7119 |
| issn_series | 0178-5354 |
| copyright | Springer-Verlag London 2003 |
发表于 2025-3-21 19:10:25
