2024年4月23日发(作者：华为鸿蒙系统最新版本)

College of Software Engineering

Undergraduate Course Syllabus

Course ID

Course

Attribute

□Compulsory ■Selective

2

Course Language

Period

□English ■Chinese

32

311021020

Course Name

Pattern Recognition

Credit Hour

Semester

□First Fall □First Spring □Second Fall □Second Spring

□Third Fall ■Third Spring □Fourth Fall □Fourth Spring

Instructors

He Kun

Description

Prerequisites

This course will mainly introduce the following knowledge to the students:

（1）

Bayes formula

Decision Theory

。

（2）

Probability density function

estimation。

（3） linear difference function。

（4） nonlinear difference function。

（5）

neighbor method.

。

（6）

empirical risk

minimization

and orderly risk

minimization method

。

（7）

Characteristics

choose and

extraction

。

（8）K-L

expansion

based

Feature Extraction

。

（9）

unsupervised

studying method。

（10）

Artificial Neural Network

。

（11）

Fuzzy Pattern Recognition

method。

（12）

statistical learning theory.

Support vector machine

。

Calculus, Probability Statistics, Linear Algebra,

Discrete Mathematics

, C Language Programming

Textbook

《Pattern Recognition》，Biao Zaoqi，Advanced education Press，2003，Second Edition.

是否原文教材？

《Pattern Recognition》，Huang Fenggang，Advanced education Press，1992

《Pattern Recognition》，Written by Caiyuanlong，Xi An Telecom engineering Press

Resource

《Pattern Recognition and Condition Monitoring》（first edition），Wen Xishen，Chansha：

National University

of Defence Technology

Press， 1997 .11

《Fuzzy Information Processing and application》（third edition），Chao Xiedong，Beijing：Science Press，2004.08

《Introduction to 》（fourth edition），Sheng qing，Changsha：

National University of Defence Technology

Press，1999.04

assignments, class participation, & term project (40%), final exam (60%)

Grading

Chapter1 Introduction

1. Object and Requirements

a．Know some basic concept of

Pattern Recognition

2. Teaching content

(1)

Concept of pattern recognition and pattern

(2)

pattern recognition system

(3)Problems related to

pattern recognition

Chapter2

Bayes

Decision Theory

1. Object and Requirements

a．Master some decision rules

b. Master the statistical decision of normal distribution

c. Know the design of

sequential classification

and

Classifier

2. Teaching content

(1)

Some general decision rules

a．Main content

Topics

Minimum Error Ratio based bayes decision theory

，

Minimum risk based Bayes Decision

，

min-max decision ,design of the classifier

b．Basic concept and knowledge points

Minimum Error Ratio based bayes decision theory

，

Minimum risk based Bayes Decision

，

min-max decision ,design of the classifier

c．Applications（capability requirements）

Understand the general decision rules

(2)

statistical decision of normal distribution

a．Main content

Definition and property of normal distribution function,

multivariate normalized probability

type

minimum error ratio bayes discriminate function

and

decision interface

b．Basic concept and knowledge points

Definition and property of normal distribution function,

bayes discriminate function

and

decision interface

c．Applications（capability requirements）

Understand the general decision rules

2