| 书目名称 | Second-Order Methods for Neural Networks |
| 副标题 | Fast and Reliable Tr |
| 编辑 | Adrian J. Shepherd |
| 视频video | |
| 丛书名称 | Perspectives in Neural Computing |
| 图书封面 |  |
| 描述 | About This Book This book is about training methods - in particular, fast second-order training methods - for multi-layer perceptrons (MLPs). MLPs (also known as feed-forward neural networks) are the most widely-used class of neural network. Over the past decade MLPs have achieved increasing popularity among scientists, engineers and other professionals as tools for tackling a wide variety of information processing tasks. In common with all neural networks, MLPsare trained (rather than programmed) to carryout the chosen information processing function. Unfortunately, the (traditional‘ method for trainingMLPs- the well-knownbackpropagation method - is notoriously slow and unreliable when applied to many prac tical tasks. The development of fast and reliable training algorithms for MLPsis one of the most important areas ofresearch within the entire field of neural computing. The main purpose of this book is to bring to a wider audience a range of alternative methods for training MLPs, methods which have proved orders of magnitude faster than backpropagation when applied to many training tasks. The book also addresses the well-known (local minima‘ problem, and explains ways in which |
| 出版日期 | Book 1997 |
| 关键词 | learning; neural networks; optimization; supervised learning; training |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4471-0953-2 |
| isbn_softcover | 978-3-540-76100-6 |
| isbn_ebook | 978-1-4471-0953-2Series ISSN 1431-6854 |
| issn_series | 1431-6854 |
| copyright | Springer-Verlag London 1997 |
发表于 2025-3-21 18:24:17
