Theory of Neural Information Processing Systems
Theory of Neural Information Processing Systems
复制标题
神经信息处理系统理论
DOI：
10.1001/archpedi.1951.02040030384005
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Peter Sollich
中科院分区：
文献类型：
--
作者：
A. Coolen;Reimer Kühn;Peter Sollich
1.神经网络导论1.总论2.分层网络3.带二元神经元的递归网络2.高级神经网络4.竞争无监督学习过程5.监督学习中的贝叶斯技术6.高斯过程7.二元分类的支持向量机3.信息论和神经网络8.测量信息9.作为信息度量的熵的识别10.香农信息论的积木11.信息论和统计推理12.神经网络的应用IV动力学宏观分析13.网络操作：宏观动力学14.在线学习的动力学15.在线梯度下降学习的动力学神经网络的平衡统计力学16.平衡统计力学的基础17.网络操作：平衡分析18.任务可实现性的加德纳理论附录A.历史和书目注释B.简明的C.中心极限定理的应用条件D.一些简单的求和恒等式E.高斯积分和概率分布F.矩阵恒等式G.基于凸性的增量分布H.基于凸性的不等式I.参数概率分布的度量J.鞍点积分参考文献
I INTRODUCTION TO NEURAL NETWORKS 1. General introduction 2. Layered networks 3. Recurrent networks with binary neurons II ADVANCED NEURAL NETWORKS 4. Competitive unsupervised learning processes 5. Bayesian techniques in supervised learning 6. Gaussian processes 7. Support vector machines for binary classification III INFORMATION THEORY AND NEURAL NETWORKS 8. Measuring information 9. Identification of entropy as an information measure 10. Building blocks of Shannon's information theory 11. Information theory and statistical inference 12. Applications to neural networks IV MACROSCOPIC ANALYSIS OF DYNAMICS 13. Network operation: macroscopic dynamics 14. Dynamics of online learning in binary perceptrons 15. Dynamics of online gradient descent learning V EQUILIBRIUM STATISTICAL MECHANICS OF NEURAL NETWORKS 16. Basics of equilibrium statistical mechanics 17. Network operation: equilibrium analysis 18. Gardner theory of task realizability APPENDICES A. Historical and bibliographical notes B. Probability theory in a nutshell C. Conditions for central limit theorem to apply D. Some simple summation identities E. Gaussian integrals and probability distributions F. Matrix identities G. The delta-distribution H. Inequalities based on convexity I. Metrics for parametrized probability distributions J. Saddle-point integration REFERENCES