DB004107-模式分析与模式识别

发布者:王源发布时间:2018-04-23浏览次数:883

研究生课程开设申请表

 开课院(系、所):  十大澳门网站赌博     

 课程申请开设类型: 新开   重开□    更名□请在内打勾,下同

课程

名称

中文

模式分析与模式识别

英文

Pattern Analysis and Pattern Recognition

待分配课程编号

DB004107

课程适用学位级别

博士

硕士


总学时

48

课内学时

48

学分

3

实践环节


用机小时


课程类别

公共基础     专业基础     专业必修     专业选修

开课院()


开课学期

春季

考核方式

A.笔试(开卷   闭卷)      B. 口试    

C.笔试与口试结合                 D. □其他

课程负责人

教师

姓名

赵力

职称

教授

e-mail

zhaoli@seu.edu.cn

网页地址


授课语言

汉语

课件地址


适用学科范围

信息类(一级)

所属一级学科名称

信息与通信工程

实验(案例)个数


先修课程

数字信号处理等

教学用书

教材名称

教材编者

出版社

出版年月

版次

主要教材

模式识别

Sergios Theodoridis

电子工业

2006

3

主要参考书

模式分类

Richard O.Duda Peter

机械工业

2004

2

模式识别

边肇祺

清华大学

2000

2

模式识别(原理方法及应用)

J.P.Marquess

电子工业

2002

2


一、课程介绍(含教学目标、教学要求等)300字以内)

 本课程要求掌握模式识别与模式分析的基础、原理、方法和应用,以及该学科领域近年来取得的一些新的研究成果和技术,提高学生解决工程实际问题的能力。要求掌握的内容主要包括:1、模式识别概论;2、贝叶斯决策理论;3、线性判别函数;4、聚类分析;5、模糊模式识别;6、神经网络识别理论及模型;7、子空间法模式识别;8、特征的选择与提取。


二、教学大纲(含章节目录):(可附页)


一、模式识别概论 

1. 模式识别的概念
2.
模式识别系统
3.
模式识别的应用   


二、贝叶斯决策理论

1. 基于最小错误率的贝叶斯决策
2.
基于最小风险的贝叶斯决策
3.
在限定一类错误率条件下使另一类错误率为最小的两类别决策
4.
最小最大决策
5.
序贯分类方法
6.
分类器设计  


三、 线性判别函数

1. 线形判别函数的基本概念

2. 感知器及其训练

3. 感知器准则函数及其梯度法

4. 最小平方和准则函数与H-K算法

5. Fisher线性判别式

6. 广义线性判决函数

7. 势函数法


四、 聚类分析
1.
相似性度量方法
2.
聚类准则
3.
两种简单的聚类算法(最近邻规则的聚类算法、最大最小距离聚类算法)
4.
快速动态聚类算法

五、模糊模式识别
1.
模糊信息处理基础
2.
模糊识别信息地获取
3.
模糊综合评判
4.
基于识别算法的模糊模式识别
5.
模糊聚类分析

六、神经网络识别理论及模型

1. 人工神经网络基本模型
2.
神经网络分类器
3.
模糊神经网络系统
4.
神经网络识别模型及相关技术

七、子空间法模式识别
1.
基本思想
2. K—L
变换的数值计算
3.
子空间分类器
4.
学习子空间法

八、特征的选择与提取
1.
特征评判标准——类别可分性判据
2.
特征选择及搜索算法
3.
特征提取方法
4.
多层感知器用于特征压缩



三、教学周历

 周次

 教学内容

 教学方式

1

 第一章第1-2小节 (赵力)

 讲授

2

 第一章第3小节 (赵力)

 讲授

3

 第二章第1-2小节(裴文江)

 讲授

4

 第二章第3-4小节(裴文江)

 讲授

5

 第二章第5-6小节(裴文江)

 讲授

6

 第三章第1-2小节(齐志)

 讲授

7

 第三章第3-4小节(齐志)

 讲授

8

 第三章第5-6小节(张毅锋)

 讲授

9

 第三章第7小节(张毅锋)

 讲授

10

 第四章第1-2小节(裴文江)

 讲授

11

 第四章第3-4小节(裴文江)

 讲授

12

 第五章第1-2小节(张毅锋)

 讲授

13

 第五章第3-5小节(张毅锋)

 讲授

14

 第六章第1-2小节(齐志)

 讲授

15

 第六章第3-4小节(齐志)

 讲授

16

 第七章第1-2小节(赵力)

 讲授

17

 第七章第3-4小节(赵力)

 讲授

18

 第八章第1-2小节(赵力)

 讲授

19

 第八章第3-4小节(赵力)

 讲授

20

 期末考试

 考试

四、主讲教师简介:

 赵力,男,1958年出生。现工作于十大澳门网站赌博,教授。主要从事语音、声频和视频信号处理、情感信息处理等方面的研究工作。


五、任课教师信息(包括主讲教师):


 任课

 教师

 学科

 (专业)

 办公

 电话

 住宅

 电话

 手机

 电子邮件

 通讯地址

 邮政

 编码

 赵力

信号与信息处理




zhaoli@seu.edu.cn

东南大学

十大澳门网站赌博

210096

裴文江

信号与信息处理





东南大学

十大澳门网站赌博

210096

齐志

信号与信息处理





东南大学

十大澳门网站赌博

210096

张毅锋

信号与信息处理





东南大学

十大澳门网站赌博

210096






Application Form For Opening Graduate Courses

School (Department/Institute)School of Information Science and Engineering

Course Type: New Open    Reopen □    Rename □Please tick in □, the same below

Course Name

Chinese

模式分析与模式识别

English

Pattern Analysis and Pattern Recognition

Course Number

DB004107

Type of Degree

Ph. D

Master


Total Credit Hours

48

In Class Credit Hours

40

Credit

3

Practice


Computer-using Hours


Course Type

Public FundamentalMajor Fundamental    Major Compulsory    □Major Elective

School (Department)

School of Information Science and Engineering

Term

Spring

Examination

A.PaperOpen-book   □ Closed-bookB. □Oral   

C. □Paper-oral Combination                       D. □ Others

Chief

Lecturer

Name

Zhao Li

Professional Title

Professor

E-mail

zhaoli@seu.edu.cn

Website


Teaching Language used in Course

Chinese

Teaching Material Website


Applicable Range of Discipline

Information

Name of First-Class Discipline

Information and communication engineering

Number of Experiment


Preliminary Courses

Digital Signal Processing

Teaching Books

Textbook Title

Author

Publisher

Year of Publication

Edition Number

Main Textbook

Pattern Recognition

Sergios Theodoridis

Electronic industry

2006

3

Main Reference Books

Speech Processing and recognition

Richard O.Duda Peter

mechanical industry

2004

2

Pattern Recognition

Bian Zhaoqi

Tsinghua University

2000

2

Pattern Recognitionprinciple, method, applications

J.P.Marquess

Electronic industry

2002

2



  1. Course Introduction (including teaching goals and requirements) within 300 words:

This course requires students master the foundation, principle, method, applications of pattern analysis and pattern recognition. The whole course can be divided into eight parts which include: introduction of pattern recognition;bayesian decision theory; linear discrimination function; clustering analyse; fuzzy pattern recognition; neutral network recognition theory and models; subspace pattern recognition; selection and acquisition of characteristics



  1. Teaching Syllabus (including the content of chapters and sections. A sheet can be attached):



.Introduction of pattern recognition

1. definition of pattern recognition

2. pattern recognition system

3. application of pattern recognition



.Bayesian decision theory

1. bayesian decision based on minimum error probability

2. bayesian decision based on minimum harzard

3. two type bayesian decision

4. minimum maximum decision

5.sequence classify method

6. design of classification


. Linear discrimination function

1. basic definition of linear discrimination function

2. perceptron and its training

3. perceptron criterion function and gradient method

4. minimum square sum of criterion function and HK algorithm

5. Fisher linear discrimination function

6. generalized linear discrimination function

7. potential function



. Clustering analyse
1.similarity measurement  
2. clustering criterion

3. two kinds of simple clustering algorithms

4.fast dynamical clustering algorithms


. Fuzzy pattern recognition
1. fuzzy information processing fundamentations

2.fuzzy recognition information acquisition

3.fuzzy evaluation

4.fuzzy pattern recognition based on recognition algorithms

5.fuzzy cluster analysis

. Neutral network recognition theory and model

1. basic model of ANN

2. classifier of neutral network

3.fuzzy neutral network system

4.neutral network recognition model and related techniques


. Subspace pattern recognisiton
1. basic idea                  
2.numerical calculation of K-L transform
3. subspace classifier             
4. subspace learning method           

. Selection and acquisition of characteristics
1. criterion standard of characteristics

2.  selection and search algorithm of characteristics
3. acquisition of characteristics                      
4. multi-level perceptron applied to characteristics compress












  1. Teaching Schedule:


Week

Course Content

Teaching Method

1

Chap Section1-Section2 (Zhao Li)

lecture

2

Chap Section3 (Zhao Li)

lecture

3

ChapⅡ Section1-Section2 (Pei Wenjiang)

lecture

4

ChapⅡ Section3-Section4 (Pei Wenjiang)

lecture

5

ChapⅡ Section5-Section6 (Pei Wenjiang)

lecture

6

Chap Section1-Section2 (Qi Zhi)

lecture

7

Chap Section3-Section4 (Qi Zhi)

lecture

8

ChapⅢ Section5-Section6 (Zhang Yifeng)

lecture

9

ChapⅢ Section7 (Zhang Yifeng)

lecture

10

ChapⅣ Section1-Section2 (Pei Wenjiang)

lecture

11

ChapⅣ Section3-Section4 (Pei Wenjiang)

lecture

12

ChapⅤ Section1-Section2 (Zhang Yifeng)

lecture

13

ChapⅤ Section3-Section5 (Zhang Yifeng)

lecture

14

Chap Section1-Section2 (Qi Zhi)

lecture

15

Chap Section3-Section4 (Qi Zhi)

lecture

16

ChapⅦ Section1-Section2 (Zhao Li)

lecture

17

ChapⅦ Section3-Section4 (Zhao Li)

lecture

18

ChapⅧ Section1-Section2 (Zhao Li)

lecture

19

ChapⅧ Section3-Section4 (Zhao Li)

lecture

20

Final Examination

exam

Note: 1.Above one, two, and three items are used as teaching Syllabus in Chinese and announced on the Chinese website of Graduate School. The four and five items are preserved in Graduate School.


2. Course terms: Spring, Autumn , and Spring-Autumn term.  

3. The teaching languages for courses: Chinese, English or Chinese-English.

4. Applicable range of discipline: public, first-class discipline, second-class discipline, and third-class discipline.

5. Practice includes: experiment, investigation, research report, etc.

6. Teaching methods: lecture, seminar, practice, etc.

7. Examination for degree courses must be in paper.

8. Teaching material websites are those which have already been announced.

9. Brief introduction of chief lecturer should include: personal information (date of birth, gender, degree achieved, professional title), research direction, teaching and research achievements. (within 100-500 words)


  1. Brief Introduction of Chief lecturer:

Zhao Li, male, born in 1958, work at school of information science and engineering, Southeast University, Professor, Research at speech, audio and video signal processing and emotional information processing.



  1. Lecturer Information (include chief lecturer)


Lecturer

Discipline

(major)

Office

Phone Number

Home Phone Number

Mobile Phone Number

Email

Address

Postcode

Zhao Li

Signal and Information processing




Zhaoli@seu.edu.cn

school of information science and engineering

210096

Pei Wenjiang

Signal and Information processing





school of information science and engineering

210096

Qi Zhi

Signal and Information processing





school of information science and engineering

210096

Zhang Yifeng

Signal and Information processing





school of information science and engineering

210096







9




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