| 书目名称 | Learning Algorithms Theory and Applications |
| 副标题 | Theory and Applicati |
| 编辑 | S. Lakshmivarahan |
| 视频video | http://file.papertrans.cn/583/582679/582679.mp4 |
| 图书封面 |  |
| 描述 | Learning constitutes one of the most important phase of the whole psychological processes and it is essential in many ways for the occurrence of necessary changes in the behavior of adjusting organisms. In a broad sense influence of prior behavior and its consequence upon subsequent behavior is usually accepted as a definition of learning. Till recently learning was regarded as the prerogative of living beings. But in the past few decades there have been attempts to construct learning machines or systems with considerable success. This book deals with a powerful class of learning algorithms that have been developed over the past two decades in the context of learning systems modelled by finite state probabilistic automaton. These algorithms are very simple iterative schemes. Mathematically these algorithms define two distinct classes of Markov processes with unit simplex (of suitable dimension) as its state space. The basic problem of learning is viewed as one of finding conditions on the algorithm such that the associated Markov process has prespecified asymptotic behavior. As a prerequisite a first course in analysis and stochastic processes would be an adequate preparation to pu |
| 出版日期 | Textbook 1981 |
| 关键词 | Algorithmus; Applications; Learning; Lernender Automat; calculus |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4612-5975-6 |
| isbn_softcover | 978-0-387-90640-9 |
| isbn_ebook | 978-1-4612-5975-6 |
| copyright | Springer-Verlag New York Inc. 1981 |
|Archiver|手机版|小黑屋|
派博传思国际
( 京公网安备110108008328)
GMT+8, 2025-7-22 21:13
发表于 2025-3-21 20:05:13
