TextBook:
Richard O. Duda, Peter E. Hart and David G. Stork,
Pattern Classification (2nd ed.)
Wiley Interscience, ISBN: 0-471-05669-3
HomeWork
howework1: 計算entropy
homework 2 100.3.8.
解壓縮密碼:xxxx
為上課教室,第一個字母大寫.
hw1.rar 內含測試資料iris.txt 及iris.h
det/目錄下為使用det()函數計算行列式的方法
可參考使用
Powerpoint
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.
H7XX
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
COS 511 FOUNDATIONS OF MACHINE LEARNING
CE 781/881
STATISTICAL PATTERN RECOGNITION
Spring 2002
*
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
MAS 622J/1.126J: PATTERN RECOGNITION AND ANALYSIS
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).