模式识别教学大纲(可编辑).doc

1、IntroductionPattern recognition techniques are used to automatically classify physical objects (handwritten characters, tissue samples) or abstract multidimensional patterns (n points in c/dimensions) into known or possibly unknown categories. A number of commercial pattern recognition systems are a

2、vailable for character recognition, handwriting recognition, document classification, fingerprint classification, speech and speaker recognition, white blood cell (leukocyte) classification, military target recognition, etc. Most machine vision systems employ pattern recognition techniques to identi

3、fy objects for sorting, inspection, and assembly. The design of a pattern recognition system requires the following modules: (i) sensing,(ii) feature extraction and selection, (iii) decision making and (iv) performance evaluation. The availability of low cost and high resolution sensors (e.g., digit

4、al cameras, microphones and scanners) and data sharing over the Internet have resulted in huge repositories of digitized documents (text, speech, image and video). Need for efficient archiving and retrieval of this data has fostered the development of pattern recognition algorithms in new applicatio

5、n domains (e.g., text, image and video retrieval, bioinformatics, and face recognition).Design of a pattern recognition system typically follows one of the following approaches: (i) template matching, (ii) statistical methods, (iii) syntactic methods and (iv) neural networks. This course will introd

6、uce the fundamentals of statistical pattern recognition with examples from several application areas. Techniques for analyzing multidimensional data of various types and scales along with algorithms for projection, dimensionality reduction, clustering and classification of data will be explained. Th

7、e course will present various approaches to exploratory data analysis and classifier design so students can make judicious choices when confronted with real pattern recognition problems. It is important to emphasize that the design of a complete pattern recognition system for a specific application

8、domain (e.g., remote sensing) requires domain knowledge, which is beyond the scope of this course. Students will use available MATLAB software library and implement some algorithms using their choice of a programming language.PrerequisitesCSE 232, MTH 314, and STT 441, or equivalent courses.Text Boo

9、kDuda, Hart and Stork, Pattern Classificatio n, Seco nd Editio n, Wiley, 2001.You may find the errata list useful.A nu mber of books on patter n recog niti on have bee n put on the Assig ned Read ing in the Engin eer ing Library .In additi on, a nu mber of journ als, in clud ing Pattern Recog niti o

10、n, Pattern Recog niti on Letters, IEEE Trans. Patter n An alysis & Machi ne In tellige nce (PAMI), IEEE Trans. Geoscie nce & Remote Sen sin g, IEEE Tran s. Image Process ing, and IEEE Trans. Speech, Audio, and Lan guage Process ing routi nely publish papers on patter n recog niti on theory a

11、nd applicati ons.Assig ned Read ingFollow ing books are on reading for CSE 802.hold in the Engin eeri ng library for assig ned?Theodoridis andKoutroumbasPatter n Recog niti on? Christopher BishopPatter n Recog niti on and Machi ne Lear ning? FukunagaIn troducti on to Statistical Patter n Recog niti

12、on? Devijver and Kittier ? Tou a nd GonzalezPatter n Recog niti on: A Statistical ApproachPattern Recognition Principles? You ng and Calvert? PavlidisClassificati on, Estimati on and Patter n Recog niti on Structural Patter n Recog niti on? Gon zalez and WintzSyn tactic Patter n Recog niti on? Oja?

13、Wata nabeSubspace Methods of Patter n Recog niti on Patter n Recog niti on: Huma n and Mecha ni cal? Jain and DubesAlgorithms for Clusteri ng Data (Dow nl oad the book)Patter n Recog niti on: Statistic, Structural and Neural? SchalkoffApproachesCourse ScheduleJan 8Introduction to Pattern Recognition

14、 (Ch 1)Statistical Patter n Recog niti on: A ReviewLecture slides: Patter n Recog niti onHW assig nedHW1 Solutio nsJan 10, 15,17Statistical Decision Theory (Ch 2)Jan 15: HW2 assig ned; HW1 dueLecture slides: Chapter 2Notes on Bayes Classificati onAn In troduction to Matlab.Jan 22Statistical Decision

15、 Theory (Ch 2)Lecture slides:Neyma Pears on RuleLin ear Discrim inant Fun cti onsJan 24, 29Parameter Estimati on (Ch 3)Bayes Estimator for multivariate Gaussia n den sity with unknown covaria nee matricesBayes Estimator under quadratic lossJan 24: HW3 assig ned; HW2 dueLecture slides: Chapter 3Jan 3

16、1Parameter Estimati on (Ch 3)Curse of Dime nsio nality (Ch 3)Coi n Toss ing ExampleA Problem of Dime nsion ality: A Simple ExampleLecture slides: Curse of Dime nsion alityFeb 5,7Comp onent an alysis and Discrim inants (Ch 3)Pr in ciple Compo nent An alysis (PCA)Pri ncipal comp onent an alysis for fa

17、ce recog niti on.Lecture slides: Comp onent An alysis & Discrim inantsFeb 5: HW4 assig ned; HW3 dueFeb 12,14,19Non parametric Tech ni ques (Ch 4)Lecture slides: Non parametric Tech niq uesA Branch and Bound Algorithm for Computi ng k-Nearest NeighborsFeb 19: HW5 assig ned; HW4 dueFeb 21Decisi on

18、 Trees (Ch 8) lecture slidesHierarchical Classifier Desig n Using Mutual In formati on -Sethi andSarvarayuduFeb 26Mid Term ExamFeb 28Project Discussi onMar 5, 7SPRING BREAKMar 12Project Proposal Due (2 pages)Lin ear Discri minant fun cti ons (Ch 5)Lecture slides: Lin ear discrim inant fun cti onsMar

19、 14,19Lin ear Discrim inant fun cti ons (Ch 5)Support Vector Machi nesMar 14: HW6 assig ned; HW5 dueMar 21,26Neural Networks (Ch 6)Lecture slidesLecture slides - 2audio file - 1 for Lecture slides - 2audio file - 2 for Lecture slides - 2audio file - 3 for Lecture slides - 2A note on compari ng class

20、ifiersA Tutorial on Artificial Neural NetworksPerforma nee evaluati on of patter n classifiers for han dwritte n characterrecog niti onMar 28, Apr 2Error Rate Estimati on, Bagg ing, Boost ing (Ch 9)Mar 28: HW7 assig ned, HW6 dueApr 4Classifier Comb in atio n (Ch 9)Lecture slides on classifier comb i

21、n ati onComb in ati on of Multiple Classifiers Usi ng Local Accuracy Estimates byWoods, Kegelmeyer and BowyerHandwriting digits recognition by combining classifiers by van Breukelen,Dui n, Tax and den HartogApr 9Feature Selecti onLecture slides on feature select ionBranch and Bound Algorithm for Fea

22、ture Subset Selection by Narendra andFukunagaFeature Selection : Evaluation, Application, and Small SamplePerforma nee by Jain and Zon gkerApr 11, 16, 18Un supervised Lear ning, Clusteri ng, and Multidime nsional Scali ng (Ch 10)April 11: HW7 dueLecture Slides: In troducti on to clusteri ngLecture S

23、lides: EM AlgorithmLecture Slides: Large scale clusteri ngTalk on Large Scale Clusteri ngData Clustering : 50 Years Beyond K-means(Download Presentation Slideshere)Graph Theoretical Methods for Detect ing and Describ ing GestaltClusters by C. Zah nA Non li near Mapp ing for Data Structure An alysis

24、by J. Sam monRepresentation and Recognition of Handwritten Digits Using DeformableTemplates by Jai n and Zon gkerApr 23Semi-supervised lear ningSemi-supervised lear ning by Xiaoj in ZhuBoostCluster by Liu, Jin and JainCon stra ined K-mea ns Clusteri ng with Backgro und Kno wledge by Wagstaff etal.Se

25、misupervised clusteri ng by seedi ng by Basu et al.Apr 25Final Project Prese ntati onFinal Project Report DueMay 1FINAL EXAM, 7:45 a.m. - 9:45 a.m., 3400 EBGradi ngCourse grade will be assig ned based on scores on six homework assig nmen ts, two exams and one project. Weights for these three comp on

26、ents are as follows: HW (25%), MID TERM EXAM (25%), FINAL EXAM (25%), PROJECT (25%). The cumulative score will be mapped to the letter grade as follows: 90% or higher: 4.0; 85% to 90%: 3.5; 80% to 85%: 3.0 and so on.Both the exams will be closed book. Makeup exams will be given ONLY if properly just

27、ified. Homework soluti ons must be turned in the class on the date they are due. Late homework soluti ons will not be accepted. Homework soluti ons should be either typed or n eatly pri nted.Please refer to MSU's policy on the In tearity of Scholarship. All homework solutions must reflect your o

28、wn work. Failure to do so will result in a grade of 0 in the course.The purpose of the project is to en able the stude nts to get some han ds-on experienee in the design, implementation and evaluation of pattern recognition algorithms. To facilitate the eompletion of the project in a semester, it is

29、 advised that stude nts work in teams of two. You are expected to evaluate differe nt preprocess ing, feature extracti on, and classificati on (in clud ing bagg ing and boost ing) approaches to achieve as high accuracy as possible on the selected classificati on task. The task for the project is des

30、cribed here. The project report should clearly explain the objective of the study, some background work on this problem, difficulty of the classification task, choice of represe ntati on, choice of classifiers, classifier comb in atio n strategies, error rate estimati on, etc. For most of the classifiers, e.g., support vector mach in es a nd n eural n etworks, software packages are available in the public doma in. Feel free to use them. Emp