Pattern Recognition

  • Created: 2015-10-18
  • 1872
Name:Pattern Recognition
No.:S081100XJ009Semester:Autumn
Hour:40Credit:2.0
Teacher:Huang,Qingming 
Introduction:
 
This is one of the speciality foundation courses for master degree candidates majoring in Computer Application Technology and Electronic Science & Technology. The research of Patter Recognition focuses on recognizing or classifying physical objects automatically with machines instead of people. It is developing fast in recent decades and has systematically constructed theories and methods which are broadly applied. This class deals with the fundamentals of concepts, methods and algorithms in pattern recognition, and emphasizes the combination of theory and practice. We introduce lots of examples to tell how these techniques can be used, avoiding bringing in many complex mathematical derivations. Students who take this course are required to grasp the basic theories and methods, and be capable to solve practical problems with them. Furthermore, it is expected to lay the foundations of innovating new approaches for the students.
Content:
 
Chapter 1. Introduction Concepts of pattern and pattern recognition, A brief history of pattern recognition, Introduction of applications, methods and systems of pattern recognition, Related mathematical knowledge
Chapter 2. Clustering Concepts of distance clustering, Clustering criteria, Hierarchical clustering algorithm, Dynamic clustering algorithm
Chapter 3. Discriminant Functions Linear discriminant functions, Generalized linear discriminant functions, Pattern space and weight space, The perceptron algorithm, Potential function
Chapter 4. Statistical Decision Theory Bayes decision criteria, Minimum error rate classification, Bayesian classification for normal distribution, Mean vector and parameter estimation of covariance matrix
Chapter 5. Feature Selection and Generation Class separability measures, Feature selection, The Karhunen-Loeve Transform
Chapter 6. Neural Networks Introduction of artificial neural network, Feedforward neural network, Recurrent neural network, Stochastic neural network, Self-organizing neural network, Application design and development of ANN
Chapter 7. Syntactic Pattern Recognition Relational operation, Formal language theory, Syntax structure parsing by automata, Primitive selection, syntax analysis and grammatical induction
Material:
 
References:
 
1. Zhaoqi Bian et al, Pattern Recognition(Second Edition), Tsinghua University Press, Beijing, 2000 2. J.P.Marques de Sa, Pattern Recognition Concepts, Methods and Applications, USA, 2002.