Richard O. Duda, Peter E. Hart and David G. Stork, 
Pattern Classification (2nd ed.)
Wiley Interscience, ISBN: 0-471-05669-3


  • howework1: 計算entropy
  • homework 2 100.3.8.
    hw1.rar 內含測試資料iris.txt 及iris.h 


  • Chapter 1: Introduction
  • Chapter 2, part 1: Bayesian Decision Theory (Sections 2.1-2.2)
  • Chapter 2, part 2: Bayesian Decision Theory (Sections 2.3-2.5)
  • Chapter 2, part 3: Bayesian Decision Theory (Sections 2-6,2-9)
  • 96.09.29. 02.pdf the password is the name of class's room.
  • Chapter 3, part 1: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)
  • Chapter 3, part 2: Maximum-Likelihood and Bayesian Parameter Estimation (part 2)
  • Chapter 3, part 3: Maximum-Likelihood and Bayesian Parameter Estimation (Section 3.10)
  • Chapter 4, part 1: Non-Parametric Classification (Sections 4.1-4.3)
  • Chapter 4, part 2: Non-Parametric Classification (Sections 4.3-4.5)
  • Chapter 5: Linear Discriminant Functions
  • Chapter 6: Multilayer Neural Networks
  • Chapter 7: Stochastic Methods
  • Chapter 8: Nonmetric Methods
  • Chapter 9: Algorithm-Independent Machine Learning
  • Chapter 10: Unsupervised Learning and Clustering

    Computer manual to accompany Pattern Classification

  • Computer manual to accompany Pattern Classification, Download previously uploaded algorithms

    Pattern Recognition Resources

    The textobook used in this course (Pattern Classification by Duda, Hart, Stork) presents classification algorithms that are implemented using a toolbox written in MATLAB. You may borrow my copy of the manual, if you like. In any case, I encourage you to look at an introdution to the toolbox, written by graduate student Nawei Chen and myself; it illustrates some of the basic pattern recognition ideas we discuss in class.

    Here is a tool for constructing test data for the Digit Classifier, by Henry Xiao (student in CISC859 in Fall 2004).

    Other Links

  • Statistical Pattern Recognition Toolbox for Matlab This toolbox implements a selection of statistical pattern recognition methods described in the monograph M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002
    Examples, Download Version 2.07, 17-jun-2007, ZIP [stprtool17jun07.zip] (4543654 bytes)
  • EE292D: Spring 2003 Statistical Learning and Pattern Classification
  • Pattern Recognition, Winter 2002-3
  • * G22-2565-001, Fall 2005: Machine Learning and Pattern Recognition [ Course Homepage | Schedule and Course Material | Mailing List ]
  • CS782 - Pattern Recognition and Applications - Spring'2003
  • Michigan State University Spring 2006 CSE 802 - Pattern Recognition and Analysis, 4 credits
  • EE547 PR
  • HST951 - Medical Decision Support - Spring 2002
  • CS 4803B/8803B Pattern Recongition
  • 18-794: PATTERN RECOGNITION THEORY, Spring 1999
  • 中興大學吳俊霖: PR
  • pr http://aimm02.cse.ttu.edu.tw/class_2005_1/PR/pr.htm COURSE
  • pr http://jhd.cai.swufe.edu.cn/pattern2005.9/pattern.htm
  • expectation maximization 說明
  • 最大期望演算法
  • 群集演算法
  • MIT: 9.913 Pattern Recognition for Machine Vision, Fall 2004
  • MIT: MAS.622J / 1.126J Pattern Recognition and Analysis Fall 2006
  • MIT: Object and Face Recognition, Spring 2001
  • MIT: www.myoops.org==> 腦與認知科學(Brain and Cognitive Sciences)
  • MIT: www.myoops.org==> 電機工程與資訊科學(Electrical Engineering and Computer Science)
  • 6.875 2005春季課程:密碼學與密碼分析(Cryptography and Cryptanalysis, Spring 2005)
  • 6.876J / 18.426J 2003春季課程:密碼學中的進階議題(Advanced Topics in Cryptography, Spring 2003)
  • 6.897 Selected Topics in Cryptography, Spring 2004
  • CISC 859 Pattern Recognition

  • ****Pattern Recognition Information
  • IAPR The International Association for Pattern Recognition. Much useful information can be found at the IAPR website. This includes information provided by the IAPR Technical Committees:

  • Document Layout Interpretation and its Application is a site that lists research groups, conferences, data sets, software, and bibliographies.
  • Here is a tutorial on the Nearest Neighbor Rule
  • Document Understanding and Character Recognition Web Server at the University of Maryland provides extensive information on conferences, jobs, mailing lists and news groups, online bibliographies, contributed source code, datasets and standards, public domain OCR resources, commercial resources, research groups, etc.
  • Related to Pattern Recogntion: CVonline, a compendium of computer vision. Covers many topics, including Hidden Markov Models (HMMs).