仿生模式识别与多权值神经元

part i review of statistics pattern recognition
chapter 1 introduction of pattern recognition
1.1 pattern recognition concept
1.2 pattern recognition system biasic processes
1.3 a brief survey of pattern recognition appro aches
1.4 scope and organization
chapter 2 kernel of statistical pattern recognition andpre-precessing
2.1 question arise
2.1.1 question expression
2.1.2 empirical risk minimization
2.1.3 generalization ability and complexity
2.2 kernel of statistical pattern recognition
2.2.1 vapnik-chervonenkis dimension
2.2.2 the bounds of generalization ability
2.2.3 the minimization of structural risk
2.3 preprocessin9
2.4 feature extraction and feature selection
2.4.1 curse of dimensionality
2.4.2 feature extraction
2.4.3 feature selection
2.5 support vector manchine
2.5.1 the optimal hyperplane under linearly separable
2.5.2 the soft spacing under linearly nonseparable
2.5.3 the kernel function under non-linear case
2.5.4 support vector machine's traits and advantages
references

part ii biomimetic pattern recognition
chapter 3 introduction
chapter 4 the foundation of biomimetic pattern recognition
4.1 overview of high-dimensional biomimetic informatics
4.1.1 the proposal of the problem of computer imaginalthinking
4.1.2 the principle of high-dimensional biomimeticinformatics
4.2 basic contents of high-dimensional biomimetic informatics
4.3 main features of high-dimensional giomimetic informatics
4 4 concepts and mathematical symbols in high-dimensionalbiomimetic informatics
4.4.1 concepts and definitions
4.4.2 mathematical symbols
4.4.3 symbolic computing methods in resolving geometry computingproblems
4.4.4 several applications in solving complicated geometrycomputing problems
4.5 some applications
4.5.1 blurred image restoration
4.5.2 uneven lighting image correction
4.5.3 removing facial makeup disturbances
chapter 5 the theory of biomimetic pattern recognition
5.1 the concept of biomimetic pattern recognition
5.2 the choice of the name
5.3 the developments of biomimetic pattern recognition
5.4 covering.the concept of recognition in biomimetic patternrecognition
5.5 the principle of homology-continuity： the starting point ofbiomimetic pattern recognition
5.6 expansionary product
5.7 experiments
5.7.1 the architecture of the face recognition system
5.7.2 umist face data
5.7.3 pre-treatment
5.7.4 the realization of svm face recognition algorithms
5.7.5 the realization of bpr face recognition algorithms
5.7.6 experiments results and analyzes
5.8 summary
chapter 6 applications
6.1 object recognition
6 2 a multi-camera human-face personal identification system
6.3 a recognition system for speaker-independent continuousspeech
6.4 summary
references

part ⅲ multi-weight neurons and networks
chapter 7 history and definations of artificial neuralnetworks
7.1 from biological neural networks to artificial neural networksand its development
7.2 some definitions and concepts of artificial neuralnetworks
7.3 unifications and divergences between array-processors andneural networks
7.4 artificial neural networks' effects on nanoelectronicalcomputational technology
chapter 8 geometric concepts of artificial neurons
8.1 mathematical expressions of common neurons and their geometricconcepts
8.2 general mathematical model of common neurons and its geometricconcept
8.3 direction basis function neuron and its geometric concept
8.4 multi-threshold neurons and networks
chapter 9 multi-weight neurons and their applications
9.1 general mathematical expression of multi-weight neurons'functions
9.2 interchangeabilities of points， vectors， hyper planes inhigh-dimensional space
9.3 effect of high-dimensional point distribution ana