具体描述
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.
用户评价
##Bishop的这本NN比Bishop的ML大法看起来轻松多了……
评分##about how to build a network of neutrons
评分##Bishop的这本NN比Bishop的ML大法看起来轻松多了……
评分##about how to build a network of neutrons
评分reference of AI.
评分 评分##很友好
评分##about how to build a network of neutrons
评分相关图书
本站所有内容均为互联网搜索引擎提供的公开搜索信息，本站不存储任何数据与内容，任何内容与数据均与本站无关，如有需要请联系相关搜索引擎包括但不限于百度，google,bing,sogou 等
© 2025 book.qciss.net All Rights Reserved. 图书大百科 版权所有