- 神经计算的信息论方法(英文版香农信息科学经典)
- - 作者：(意)古斯塔沃·德科//(德)德拉甘·奥布拉多维奇|责编:陈亮//刘叶青
  - 出版社：世图出版公司
  - ISBN：9787519296971
  - 出版日期：2023/01/01
  - 页数：261
- 售价：35.6

内容大纲
神经计算的方法由于其具有学习能力、可以实现自组织，非线性映射等特点，丰富了传统模式分类的模型和算法，开辟了模式识别发展的新途径。研究模式分类的神经计算方法，无论对神经计算理论的发展，还是对模式分类技术的实际应用，都具有重要的意义。
本书分为两个部分十章内容，针对不同的模式分类问题，根据其具体处理的数据特点和实现目标的不同，采用神经计算的理论和方法对其进行研究。
作者介绍
目录
Acknowledgments
Foreword
  CHAPTER 1  Introduction
  CHAPTER 2  Preliminaries of Information Theory and Neural Networks
    2.1  Elements of Information Theory
      2.1.1  Entropy and Information
      2.1.2  Joint Entropy and Conditional Entropy
      2.1.3  Kullback-Leibler Entropy
      2.1.4  Mutual Information
      2.1.5  Differential Entropy, Relative Entropy and Mutual Information
      2.1.6  Chain Rules
      2.1.7  Fundamental Information Theory Inequalities
      2.1.8  Coding Theory
    2.2  Elements of the Theory of Neural Networks
      2.2.1  Neural Network Modeling
      2.2.2  Neural Architectures
      2.2.3  Learning Paradigms
      2.2.4  Feedforward Networks: Baekpropagation
      2.2.5  Stochastic Recurrent Networks: Boltzmann Machine
      2.2.6  Unsupervised Competitive Learning
      2.2.7  Biological Learning Rules
PART Ⅰ: Unsupervised Learning
  CHAPTER 3  Linear Feature Extraction: Infomax Principle
    3.1  Principal Component Analysis: Statistical Approach
      3.1.1  PCA and Diagonalization of the Covariance Matrix
      3.1.2  PCA and Optimal Reconstruction
      3.1.3  Neural Network Algorithms and PCA
    3.2  Information Theoretic Approach: Infomax
      3.2.1  Minimization of Information Loss Principle and Infomax Principle
      3.2.2  Upper Bound of Information Loss
      3.2.3  Information Capacity as a Lyapunov Function of the General Stochastic Approximation
  CHAPTER 4  Independent Component Analysis: General Formulation and Linear Case
    4.1  ICA-Definition
    4.2  General Criteria for ICA
      4.2.1  Cumulant Expansion Based Criterion for ICA
      4.2.2  Mutual Information as Criterion for ICA
    4.3  Linear ICA
    4.4  Gaussian Input Distribution and Linear ICA
      4.4.1  Networks With Anti-Symmetric Lateral Connections
      4.4.2  Networks With Symmetric Lateral Connections
      4.4.3  Examples of Learning with Symmetric and Anti-Symmetric Networks
    4.5  Learning in Gaussian ICA with Rotation Matrices: PCA
      4.5.1  Relationship Between PCA and ICA in Gaussian Input Case
      4.5.2  Linear Gaussian ICA and the Output Dimension Reduction
    4.6  Linear ICA in Arbitrary Input Distribution
      4.6.1  Some Properties of Cumulants at the Output of a Linear Transformation
   4.6.2 The Edgeworth Expansion Cri
    5.1  Infomax Principle for Boltzmann Machines
      5.1.1  Learning Model
      5.1.2  Examples of Infomax Principle in Boltzmann Machine
    5.2  Redundancy Minimization and Infomax for the Boltzmann Machine
      5.2.1  Learning Model
      5.2.2  Numerical Complexity of the Learning Rule
      5.2.3  Factorial Learning Experiments
      5.2.4  Receptive Fields Formation from a Retina
    5.3  Appendix
  CHAPTER 6  Nonfinear Feature Extraction: Deterministic Neural Networks
    6.1  Redundancy Reduction by Triangular Volume Conserving Architectures
      6.1.1  Networks with Linear, Sigraoidal and Higher Order Activation Functions
      6.1.2  Simulations and Results
    6.2  Unsupervised Modeling of Chaotic Time Series
      6.2.1  Dynamical System Modeling
    6.3  Redundancy Reduction by General Symplectic Architectures
      6.3.1  General Entropy Preserving Nonlinear Maps
      6.3.2  Optimizing a Parameterized Symplectic Map
      6.3.3  Density Estimation and Novelty Detection
    6.4  Example: Theory of Early Vision
      6.4.1  Theoretical Background
      6.4.2  Retina Model
PART Ⅱ:  Supervised Learning
  CHAPTER 7  Supervised Learning and Statistical Estimation
    7.1  Statistical Parameter Estimation- Basic Definitions
      7.1.1  Cramer-Rao Inequality for Unbiased Estimators
    7.2  Maximum Likelihood Estimators
      7.2.1  Maximum Likelihood and the Information Measure
    7.3  Maximum A Posteriori Estimation
    7.4  Extensions of MLE to Include Model Selection
      7.4.1  Akaike's Information Theoretic Criterion (A1C)
      7.4.2  Minimal Description Length and Stochastic Complexity
    7.5  Generalization and Learning on the Same Data Set
  CHAPTER 8  Statistical Physics Theory of Supervised Learning and GeneraliTation
    8.1  Statistical Mechanics Theory of Supervised Learning
      8.1.1  Maximum Entropy Principle
      8.1.2  Probability Inference with an Ensemble of Networks
      8.1.3  Information Gain and Complexity Analysis
    8.2  Learning with Higher Order Neural Networks
      8.2.1  Partition Function Evaluation
      8.2.2  Information Gain in Polynomial Networks
      8.2.3  Numerical Experiments
    8.3  Learning with General Feedforward Neural Networks
      8.3.1  Partition Function Approximation
      8.3.2  Numerical E
    9.2  Composite Models as Gaussian Mixtures
  CHAPTER 10  Information Theory Based Regularizing Methods
    10.1  Theoretical Framework
      10.1.1  Network Complexity Regulation
      10.1.2  Network Architecture and Learning Paradigm
      10.1.3  Applications of the Mutual Information Based Penalty Term
    10.2  Regularization in Stochastic Potts Neural Network
      10.2.1  Neural Network Architecture
      10.2.2  Simulations
References
Index

内容大纲

作者介绍

目录

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