期刊文献+

神经网络的统计学习理论基础 被引量:1

The Theory Elements of Neural Network Statistical Learning
下载PDF
导出
摘要 介绍神经网络的统计学习过程和理论,讨论基于经验风险最小化的学习理论对神经网络推广性能的影响,分析基于结构风险最小化的支持向量机.认为神经网络因其出色的高度非线性映射能力、自组织和适应能力、记忆联想能力,使得神经网络成为机器学习的重要研究领域. The statistical learning process and theory of the neural network are introduced.The influence of generation ability based on the empirical risk minimization and the support vector machines based on the structural risk minimization are discussed.The neural network becomes a research hotspot in machine learning because of its outstanding nonlinear mapping,self-organized,parallelity, adaptation.
作者 吴建生 金龙
出处 《广西科学院学报》 2005年第2期102-105,109,共5页 Journal of Guangxi Academy of Sciences
基金 广西自然科学基金 (0 3 3 90 2 5 )资助项目
  • 相关文献

参考文献8

二级参考文献115

  • 1董聪.大脑、知觉模型和计算机模拟[J].科技导报,1997,15(7):7-10. 被引量:8
  • 2袁亚湘 孙文瑜.最优化理论与方法[M].北京:科学出版社,1999..
  • 3加肇祺.模式识别[M].北京:清华大学出版社,1988..
  • 4Jensen F V. An Introduction to Bayesian Networks [ M ]. New York: Springer, 1996.
  • 5Jensen F V. Bayesian Networks and Decision Graphs [ M]. New York: Springer, 2001.
  • 6Pearl J. Graphical Models for Probabilistic and Causal Reasoning[ A]. The Computer Science and Engineering Handbook [ M ].Boca Raton, FL, USA : CRC Press, 1997, Volume 1. 697 - 714.
  • 7Lauritzen S. Graphical Models [ M]. Oxford: Oxford University Press, 1996.
  • 8Cowell R G, Dawid A P, Lauritzen S L,et al. Probabilistic Networks and Expert Systems [M]. New York: Springer, 1999.
  • 9Huang C, Darwiche A. Inference in belief networks : a procedural guide [ J ] , lnternation',d Journal of Approximate Reasoning,1996,15 ( 3 ) : 225 - 263.
  • 10Dawid A P. Applications of a general propagation algorithm for probabilistic expert systems [ J ]. Statistics and Computing,1992,2(2) :25 -36.

共引文献2437

同被引文献12

引证文献1

二级引证文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部