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  • Main Page
    :[[ECE662|ECE662: "Statistical Decision Theory and Pattern Recognition"]]
    2 KB (209 words) - 13:07, 9 September 2022
  • List of Course Wikis
    :[[2016_Spring_ECE_662_Boutin| ECE662: "Pattern Recognition and Decision Making Processes", Prof. Boutin]] :[[2014_Summer_pattern_recognition_Bochum_Boutin|Bochum Statistical Pattern Recognition]]
    13 KB (1,570 words) - 13:53, 7 August 2018
  • 2014 Summer pattern recognition Bochum Boutin
    [[Category:pattern recognitionBochumSummer2014Boutin]] =Statistical Pattern Recognition (Bochum)=
    215 B (24 words) - 01:55, 8 July 2014
  • Meta Course List
    :[[ECE662|ECE662: "Statistical Decision Theory and Pattern Recognition"]] *[[ECE662|ECE 662]]: "Pattern recognition and Decision Making Processes
    4 KB (474 words) - 07:08, 4 November 2013
  • ECE662:BoutinSpring08 Old Kiwi
    [[Category:pattern recognition]] ...he page for the Spring 2008 edition of the course [[ECE662|ECE662: Pattern Recognition and Decision Making processes]].
    6 KB (747 words) - 05:18, 5 April 2013
  • ECE662:Glossary Old Kiwi
    When we present a trained recognition system with an unknown pattern and ask it to classify it, we call that testing. When we present a pattern recognition with a set of classified patterns so that it can learn the characteristics
    31 KB (4,832 words) - 18:13, 22 October 2010
  • Clustering Old Kiwi
    ..."Dynamic Cluster Formation Using Level Set Methods", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 28, NO. 6, JUNE 2006.''' * K. Fukunaga, Introduction to Statistical Pattern Recognition, second ed. Boston Academic Press, 1990.
    8 KB (1,173 words) - 12:41, 26 April 2008
  • Homework 2 Old Kiwi
    d). Serval matlab codes realated to learning, clustering, and pattern classification : It contains lots of pattern recognition algorithms and gives the description and pesudo code of them.
    5 KB (746 words) - 16:33, 17 April 2008
  • Lecture 1 - Introduction Old Kiwi
    == Definition and Examples of Pattern Recognition == Main article: [[What is Pattern Recognition_Old Kiwi]].
    3 KB (468 words) - 08:45, 17 January 2013
  • Lecture 2 - Decision Hypersurfaces Old Kiwi
    ...obtaining an optimum decision tree is another difficult problem in pattern recognition.
    5 KB (737 words) - 08:45, 17 January 2013
  • Lecture 8 - MLE, BPE and Linear Discriminant Functions Old Kiwi
    ...ata. However, large training datasets are typically unavailable in pattern recognition problems. Furthermore, when the dimension of data increases, the complexity (From [["Pattern Classification" by Duda, Hart, and Stork_Old Kiwi|DHS]] Chapter 5)
    5 KB (792 words) - 08:48, 17 January 2013
  • Lecture 13 - Kernel function for SVMs and ANNs introduction Old Kiwi
    ...example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological
    8 KB (1,354 words) - 08:51, 17 January 2013
  • Textbooks Old Kiwi
    === Introduction to Statistical Pattern Recognition === * One of the three classical books on pattern recognition
    3 KB (340 words) - 17:58, 6 March 2008
  • Support Vector Machines Old Kiwi
    ...pdf/burges98tutorial.pdf A Tutorial on Support Vector Machines for Pattern Recognition] ...arAuthor=Pontil%2C+M.%3B+Verri%2C+A. Support Vector Machines for 3D Object Recognition]
    3 KB (366 words) - 08:48, 10 April 2008
  • ECE662:ChangeLog Old Kiwi
    * 2008/04/25 -- Addeda pattern recognition application field in remote sensing ...thored about 3 feature-based automotive [[Feature Extraction_Old Kiwi|lane recognition]].
    10 KB (1,418 words) - 12:21, 28 April 2008
  • What is Pattern Recognition Old Kiwi
    Definition of Pattern Recognition: Pattern Recognition is the art of assigning classes or categories to data. When the computer i
    3 KB (436 words) - 08:43, 10 April 2008
  • Estimation of Classifiability Old Kiwi
    Estimation of classifiability of various Pattern Recognition techniques is needed to compare them against each other and also to select
    1 KB (170 words) - 08:46, 10 April 2008
  • Parametric Estimators Old Kiwi
    ...ating parameters that are the center of a heated debate within the pattern recognition community. These methods are Maximum Likelihood Estimation (MLE) and Bayes
    6 KB (995 words) - 10:39, 20 May 2013
  • Tools Old Kiwi
    ...ver-studio-freeware-wfhucunj.html] - This is a very simple to use pattern recognition tool. The tool uses artificial intelligence techniques like neural networks ...cognition, face recognition, speech recognition, computer vision, and coin recognition.
    2 KB (307 words) - 17:13, 21 April 2008
  • Scilab Old Kiwi
    Here you can find relevant information on how to implement Pattern Recognition projects using Scilab. * Scilab Toolbox especialized on Pattern Recognition -- Presto-Box [http://www.scilab.org/contrib/index_contrib.php?page=display
    3 KB (376 words) - 20:45, 26 March 2008
  • The R Project Old Kiwi
    ...ould focus on the ones related to the several machine learning and pattern recognition techniques related to the course. Please, follow the links below to more in == Pattern recognition related tutorials ==
    2 KB (241 words) - 23:32, 11 March 2008
  • "Pattern Recognition and Neural Networks" by Brian Ripley Old Kiwi
    ...It will be valuable for understanding some of the applications of pattern recognition
    413 B (53 words) - 23:42, 11 March 2008
  • "Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler Old Kiwi
    ...A great reference if you are interested in the mathematics behind pattern recognition
    479 B (60 words) - 23:43, 11 March 2008
  • "Pattern Classification" by Duda, Hart, and Stork Old Kiwi
    One of the three classical books on pattern recognition. A great reference if you are going to be using the algorithms
    395 B (51 words) - 23:45, 11 March 2008
  • "Introduction to Statistical Pattern Recognition" by K. Fukunaga Old Kiwi
    =Official reference for [[ECE662]]: "Pattern Recognition Theory and Decision Making Processes".= ...or the course. A great reference if you are going to be developing pattern recognition algorithms
    917 B (96 words) - 05:06, 25 August 2010
  • Pattern Recognition Journals Old Kiwi
    A set of journals on pattern recognition and related areas is listed. ...tem.c5efb9b8ade9096b8a9ca0108bcd45f3/index.jsp?&pName IEEE Transactions on Pattern Analysis and Machine Intelligence]
    1 KB (178 words) - 11:46, 16 March 2008
  • Pattern Recognition Conferences Old Kiwi
    ===IEEE Pattern Recognition Conferences 2008=== * [http://www.icpr2008.org/ 19th International Conference on Pattern Recognition, Tampa 2008]
    532 B (64 words) - 12:00, 16 March 2008
  • Publications Old Kiwi
    ==== ''A good review paper on Statistical Pattern Recognition:'' ==== ...ang Mao, "Statistical Pattern Recognition: A Review," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4-37, Jan., 2000
    39 KB (5,715 words) - 10:52, 25 April 2008
  • Comparison of MLE and Bayesian Parameter Estimation Old Kiwi
    ...ating parameters that are the center of a heated debate within the pattern recognition community. These methods are Maximum Likelihood Estimation (MLE) and Bayes
    2 KB (287 words) - 10:39, 20 May 2013
  • Applications of Pattern Recognition Old Kiwi
    ...tioning the field, please explain in detail how a specific tool of pattern recognition can be used in research. * automatic speech recognition
    6 KB (905 words) - 12:18, 28 April 2008
  • Gaussian distribution Old Kiwi
    ...variables approximate the normal distribution. So, considering the pattern recognition case, there will be very large set of feature vectors and classes, and inde
    2 KB (247 words) - 08:32, 10 April 2008
  • Scilab - resources related to Scilab Old Kiwi
    Here you can find relevant information on how to implement Pattern Recognition projects using Scilab. ...page=displayContribution&fileID=194 Scilab Toolbox especialized on Pattern Recognition -- Presto-Box]
    3 KB (379 words) - 10:20, 20 March 2008
  • Bayes Decision Rule Old Kiwi
    [[Category:pattern recognition]]
    1 KB (172 words) - 11:08, 10 June 2013
  • Case-based Reasoning Old Kiwi
    ...d achievements, is to help us to have an insight on how we can use pattern recognition techniques for AI. ...e' statements in a C or C++ program. In RBR we are trying to match a given pattern of features to a rule; the 'if' condition. If we find a match, those rules
    6 KB (1,055 words) - 11:14, 7 April 2008
  • Fisher Linear Discriminant Old Kiwi
    ...79/00982904.pdf Two variations on Fisher's linear discriminant for pattern recognition]" is a nice journal article. The paper provides two fast and simple techni ...79/00982904.pdf Two variations on Fisher's linear discriminant for pattern recognition].
    3 KB (475 words) - 18:05, 28 March 2008
  • Image Processing Old Kiwi
    ===Face Recognition=== ...rl=/iel4/5726/15322/00711956.pdf]. Also variants of FLD are used for face recognition such as DiaFLD [http://linkinghub.elsevier.com/retrieve/pii/S09252312060008
    4 KB (549 words) - 14:40, 25 April 2008
  • Curse of Dimensionality Old Kiwi
    [[Category:pattern recognition]]
    831 B (120 words) - 10:57, 10 June 2013
  • Editing technique Old Kiwi
    Reference: Andrew Webb, "Statistical Pattern Recognition" , 2nd, Wiley, 2001.
    2 KB (296 words) - 11:48, 7 April 2008
  • Fuzzy C-means Old Kiwi
    Fuzzy c-means method (Bezdek, 1981) is frequently used in pattern recognition. It is based on minimization of the following objective function, with resp
    1 KB (258 words) - 16:20, 10 April 2008
  • Video Classification - State of the Art Old Kiwi
    Motion Pattern-Based Video Classification and Retrieval video--> audio--> text(speech) recognition-> keywords
    2 KB (222 words) - 12:12, 11 April 2008
  • Spectral Methods Old Kiwi
    ...dimensionality in order to enable a more effective use of several pattern recognition techniques such as clustering algorithms. Here we review the most popular s
    2 KB (238 words) - 10:41, 28 April 2008
  • MDS Old Kiwi
    ...ation of high dimensional data is important in several subareas of pattern recognition including the verification of cluster validity. In order to visualize high
    3 KB (582 words) - 12:55, 21 April 2008
  • Testing Old Kiwi
    When we present a trained recognition system with an unknown pattern and ask it to classify it, we call that testing.
    117 B (21 words) - 23:25, 24 April 2008
  • Training Old Kiwi
    When we present a pattern recognition with a set of classified patterns so that it can learn the characteristics
    145 B (26 words) - 23:27, 24 April 2008
  • Recommendations for nonlinear discrimination methods Old Kiwi
    ...sing non-linear discrimination techniques in the book "Statistical Pattern Recognition" by Andrew Webb. In this page, I will summarize the main points of the reco #Try simple pattern recognition technique (K-nearest neighbor, linear discriminant analysis)first as a base
    2 KB (311 words) - 10:49, 26 April 2008
  • ECE490W:ECE490W Old Kiwi
    ...r the maymester course ECE490W: Introduction to Machine Learning & Pattern Recognition (in Turkey).
    473 B (74 words) - 17:51, 5 May 2008
  • ECE662:ECE662 Old Kiwi
    This is the kiwi page for the course ECE662: Pattern Recognition and Decision Making Processes
    377 B (57 words) - 18:05, 5 May 2008
  • Lecture 2 - Decision Hypersurfaces OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    5 KB (744 words) - 11:17, 10 June 2013
  • ECE662:BoutinSpring08 OldKiwi
    ...he page for the Spring 2008 edition of the course [[ECE662|ECE662: Pattern Recognition and Decision Making processes]]. * [[What is Pattern Recognition_OldKiwi|What is Pattern Recognition]]
    7 KB (875 words) - 07:11, 13 February 2012
  • ECE662
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    3 KB (429 words) - 09:07, 11 January 2016
  • Test
    [[Category:pattern recognition]] [2] R. O. Duda, P. E. Hart, and D. G. Stork. ''Pattern Classification (2nd Edition)''. Wiley-Interscience, 2000.
    17 KB (2,590 words) - 10:45, 22 January 2015
  • Lecture 9 - Linear Discriminant Functions OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    9 KB (1,341 words) - 11:15, 10 June 2013
  • KenTobinORNLmboutinOct09
    ...y of our programmatic activities is multi-modal image analysis and pattern recognition. MSSE has a strong history in the development of imaging technologies and
    3 KB (507 words) - 08:30, 22 October 2009
  • Image Morphing
    ...representations of image morphing. Morphing of images is based on pattern recognition and co-ordinate transformation(Bilinear transformation).
    2 KB (262 words) - 03:50, 7 December 2009
  • Peer legacy
    *[[Peer_Legacy_ECE662|ECE662: Statistical Pattern Recognition and Decision Making Processes]]
    4 KB (489 words) - 06:24, 24 April 2012
  • 2010 Spring ECE 662 mboutin
    = [[ECE662]]: "Statistical Pattern Recognition and Decision Making Processes", Spring 2010 = *[["Introduction to Statistical Pattern Recognition" by K. Fukunaga OldKiwi]] (This is the main reference)
    4 KB (547 words) - 12:24, 25 June 2010
  • ECE662 topic1 discussions
    ...gating possible models to incorporate existing Computer Vision and Pattern Recognition methods on Object Detection to infer human activity intention. [[User:Yuanl ...hope I could get a comprehensive understanding of the area of Pattern Recognition. --Hengzhou
    8 KB (1,359 words) - 04:54, 6 May 2010
  • OutlineECE662S10
    | 2. What is pattern Recognition
    1 KB (165 words) - 08:55, 22 April 2010
  • Lecture1ECE662S10
    ...e then talked about "[[What_is_Pattern_Recognition_OldKiwi|what is pattern recognition?]]".
    500 B (67 words) - 07:47, 12 April 2010
  • What is Pattern Recognition OldKiwi
    [[Category:pattern recognition]] =What is Pattern Recognition?=
    3 KB (462 words) - 10:41, 10 June 2013
  • Bayes Rate Fallacy: Bayes Rules under Severe Class Imbalance
    ...n on the class imbalance problem has been given by the statistical pattern recognition community, as shown in [2].
    5 KB (694 words) - 12:41, 2 February 2012
  • Support Vector Machines
    ...pdf/burges98tutorial.pdf A Tutorial on Support Vector Machines for Pattern Recognition] ...arAuthor=Pontil%2C+M.%3B+Verri%2C+A. Support Vector Machines for 3D Object Recognition]
    3 KB (416 words) - 10:56, 13 April 2010
  • Homework 2 OldKiwi
    d). Serval matlab codes realated to learning, clustering, and pattern classification : It contains lots of pattern recognition algorithms and gives the description and pesudo code of them.
    5 KB (761 words) - 10:53, 13 April 2010
  • Decision Theory Glossary ECE662Spring2008
    When we present a trained recognition system with an unknown pattern and ask it to classify it, we call that testing. When we present a pattern recognition with a set of classified patterns so that it can learn the characteristics
    31 KB (4,787 words) - 18:21, 22 October 2010
  • Chess
    ...chess playing is an application of "artificial Intelligence" and "Pattern Recognition". ...tern Recognition and decision making processes"]] to learn what is pattern recognition.
    6 KB (983 words) - 22:31, 9 February 2011
  • Maymester Intro to Machine learning 2011
    = [[SA1050|SA 10503]]: Introduction to Machine Learning & Pattern Recognition=
    4 KB (513 words) - 16:07, 4 November 2010
  • SA1050
    = SA 10503: Introduction to Machine Learning & Pattern Recognition= * [[About Pattern Recognition]]
    1 KB (164 words) - 06:47, 18 November 2010
  • ECE
    *[[ECE662|ECE 662]]: "Pattern recognition and Decision Making Processes
    3 KB (380 words) - 18:29, 9 January 2015
  • Machine learning slectures
    ..._Spring2008_sLecture_collective|The Boutin Lectures on Statistical Pattern Recognition, 2010]] ...l_Pattern_recognition_slectures|The Boutin Lectures on Statistical Pattern Recognition, 2014]]
    1 KB (140 words) - 12:14, 27 March 2015
  • West Lafayette Scholastic November2010 Chess Tournament
    *Pattern recognition
    2 KB (218 words) - 06:24, 19 November 2010
  • Lecture1ECE662S12
    ...nts in the class are working in image processing. We noted that in pattern recognition, one always chooses among a 'finite' set of classes, labeled 1,2,... n. (Ho We used a toy problem to illustrate the statistical pattern recognition paradigm. In this toy problem, a game show host is asking a contestant to g
    3 KB (490 words) - 12:30, 23 February 2012
  • 2012 Spring ECE 662 Boutin
    = [[ECE662]]: "Pattern Recognition and Decision Making Processes", Spring 2012 = ...2=sXtX5ecwEdHxoViGqEcGYA A tutorial on support vector machines for pattern recognition]
    4 KB (514 words) - 07:20, 25 June 2012
  • Lecture2ECE662S12
    ...en replaced this flow chart by a more complex one representing the pattern recognition paradigm from an engineering perspective, where the data acquisition and pr
    2 KB (358 words) - 12:30, 23 February 2012
  • Hw1 ECE662 S12
    [[Category:pattern recognition]]
    918 B (134 words) - 13:18, 8 March 2012
  • Hw1 discussion ECE662 S12
    [[Category:pattern recognition]]
    2 KB (320 words) - 12:21, 12 February 2012
  • Lecture 1 - Introduction OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    3 KB (413 words) - 11:17, 10 June 2013
  • Lecture 3 - Bayes classification OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    6 KB (874 words) - 11:17, 10 June 2013
  • Lecture 4 - Bayes Classification OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    8 KB (1,403 words) - 11:17, 10 June 2013
  • Bayes Classification: Experiments and Notes OldKiwi
    [[Category:pattern recognition]]
    5 KB (772 words) - 11:05, 10 June 2013
  • Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    10 KB (1,609 words) - 11:22, 10 June 2013
  • Lecture 17 - Nearest Neighbors Clarification Rule and Metrics OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' * e.g. fingerprint-based recognition
    6 KB (977 words) - 11:22, 10 June 2013
  • Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    7 KB (1,098 words) - 11:22, 10 June 2013
  • Lecture 6 - Discriminant Functions OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    10 KB (1,604 words) - 11:17, 10 June 2013
  • Lecture 7 - MLE and BPE OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    10 KB (1,472 words) - 11:16, 10 June 2013
  • Lecture 5 - Discriminant Functions OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    6 KB (946 words) - 11:17, 10 June 2013
  • Lecture 8 - MLE, BPE and Linear Discriminant Functions OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    6 KB (833 words) - 11:16, 10 June 2013
  • Lecture 10 - Batch Perceptron and Fisher Linear Discriminant OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    6 KB (813 words) - 11:18, 10 June 2013
  • Lecture 11 - Fischer's Linear Discriminant again OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    6 KB (946 words) - 11:18, 10 June 2013
  • Lecture 12 - Support Vector Machine and Quadratic Optimization Problem OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    8 KB (1,278 words) - 11:19, 10 June 2013
  • Lecture 13 - Kernel function for SVMs and ANNs introduction OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' ...example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological
    9 KB (1,389 words) - 11:19, 10 June 2013
  • Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    13 KB (2,098 words) - 11:21, 10 June 2013
  • Lecture 15 - Parzen Window Method OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    8 KB (1,246 words) - 11:21, 10 June 2013
  • UCI database with versatile categories of data
    UCI database is a versatile body of pattern recognition data with associated literature and analysis.
    730 B (126 words) - 19:14, 7 February 2012
  • UCI Data Set: Data sorted according to categories(Life Sciences, Physical Sciences etc.)
    UCI database is a versatile body of pattern recognition data with associated literature and analysis.
    740 B (127 words) - 19:54, 7 February 2012
  • Lecture 19 - Nearest Neighbor Error Rates OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    6 KB (1,041 words) - 11:22, 10 June 2013
  • Lecture 20 - Density Estimation using Series Expansion and Decision Trees OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    7 KB (1,082 words) - 11:23, 10 June 2013
  • Lecture 21 - Decision Trees(Continued) OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' defined as the "minimum probability that a training pattern is misclassified"
    7 KB (1,055 words) - 11:23, 10 June 2013
  • Lecture 22 - Decision Trees and Clustering OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' * Example of Index: Face Recognition
    6 KB (837 words) - 11:23, 10 June 2013
  • Lecture 23 - Spanning Trees OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    7 KB (1,091 words) - 11:23, 10 June 2013
  • Lecture 24 - Clustering and Hierarchical Clustering OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Category:pattern recognition]]
    9 KB (1,276 words) - 11:24, 10 June 2013
  • Lecture 25 - Clustering Algorithms OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' Examnple with 6 pattern <math>X_1 , X_2 , \cdots , X_6</math>
    8 KB (1,299 words) - 11:24, 10 June 2013
  • Hw2 ECE662 S12
    [[Category:pattern recognition]]
    1 KB (164 words) - 14:25, 30 May 2012
  • Parametric Estimators OldKiwi
    ...ating parameters that are the center of a heated debate within the pattern recognition community. These methods are Maximum Likelihood Estimation (MLE) and Bayes
    6 KB (976 words) - 13:25, 8 March 2012
  • Lecture19ECE662S12
    [[Category:pattern recognition]] ...sity estimation technique, along with the k-nearest neighbor (KNN) pattern recognition method. More specifically, we presented a formula for estimating a density
    2 KB (274 words) - 10:34, 22 March 2012
  • Lecture 26 - Statistical Clustering Methods OldKiwi
    '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' *Assume each pattern <math>x_{i}\in D</math> was drawn from a mixture <math>c</math> underlying
    8 KB (1,214 words) - 11:24, 10 June 2013
  • Lecture 27 - Clustering by finding valleys of densities OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    8 KB (1,313 words) - 11:24, 10 June 2013
  • Lecture 28 - Final lecture OldKiwi
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    10 KB (1,704 words) - 11:25, 10 June 2013
  • Lecture20ECE662S12
    [[Category:pattern recognition]]
    2 KB (269 words) - 03:40, 12 April 2012
  • Lecture21ECE662S12
    [[Category:pattern recognition]]
    2 KB (259 words) - 03:57, 12 April 2012
  • Hw3 ECE662 S12
    [[Category:pattern recognition]] ==Automatic Pattern Recognition Contest!==
    25 KB (2,524 words) - 07:19, 25 June 2012
  • Lecture22ECE662S12
    [[Category:pattern recognition]]
    1 KB (181 words) - 03:57, 12 April 2012
  • Lecture23ECE662S12
    [[Category:pattern recognition]]
    1 KB (159 words) - 10:01, 23 April 2012
  • Lecture24ECE662S12
    [[Category:pattern recognition]]
    1 KB (146 words) - 04:01, 12 April 2012
  • Lecture25ECE662S12
    [[Category:pattern recognition]]
    1 KB (166 words) - 04:02, 12 April 2012
  • Lecture26ECE662S12
    [[Category:pattern recognition]]
    1 KB (177 words) - 03:49, 19 April 2012
  • Lecture27ECE662S12
    [[Category:pattern recognition]]
    1 KB (181 words) - 03:51, 19 April 2012
  • Hw3 discussion ECE662 S12
    ...e best students in the world in one of the coolest field of study, pattern recognition! Which classifier will make a better prediction for this data, SVM, Bayes,
    1 KB (219 words) - 11:33, 20 April 2012
  • MLE Examples: Binomial and Poisson Distributions OldKiwi
    [[Category:pattern recognition]]
    2 KB (310 words) - 09:58, 23 April 2012
  • MLE Examples: Exponential and Geometric Distributions OldKiwi
    [[Category:pattern recognition]]
    3 KB (446 words) - 10:00, 23 April 2012
  • Honors project 1 ECE302S12
    [[Category:Pattern Recognition]] ...ttp://www.amazon.com/Pattern-Classification-2nd-Richard-Duda/dp/0471056693 Pattern Classification 2nd edition] by Duda, Hart and Stork. It is a really good bo
    2 KB (236 words) - 18:36, 12 February 2015
  • Introduction To Pattern Recognition and Classification
    [[Category:Pattern recognition]] ...e need to first understand the different components that make up a pattern recognition system. <br>
    4 KB (691 words) - 16:46, 15 February 2013
  • ECE662 Pattern Recognition Decision Making Processes Spring2008 sLecture collective
    [[Category:pattern recognition]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''
    3 KB (425 words) - 09:59, 4 November 2013
  • Course info ECE662 Boutin Spring2010
    [[Category:pattern recognition]]
    847 B (111 words) - 10:50, 10 June 2013
  • Slectures
    ...cision_Making_Processes_Spring2008_sLecture_collective|Statistical Pattern Recognition Lecture Notes]], collectively written by the students in Prof. Boutin's cla
    1 KB (196 words) - 05:26, 23 July 2013
  • Decision Surfaces OldKiwi
    [[Category:pattern recognition]]
    2 KB (245 words) - 10:43, 10 June 2013
  • Textbooks OldKiwi
    [[Category:pattern recognition]] === Introduction to Statistical Pattern Recognition ===
    3 KB (372 words) - 10:39, 10 June 2013
  • KNN Algorithm OldKiwi
    [[Category:pattern recognition]]
    5 KB (833 words) - 03:31, 19 April 2013
  • Student projects
    **[[Introduction To Pattern Recognition and Classification]]
    3 KB (389 words) - 18:10, 23 February 2015
  • 2014 Spring ECE 662 Boutin
    == [[ECE662]]: '''Statistical Pattern Recognition and Decision Making Processes, Spring 2014''' (cross-listed with CS662) == ***[[From Bayes Theorem to Pattern Recognition via Bayes Rule|Text slecture in English]] by [http://varunvasudevan.com/ Va
    10 KB (1,450 words) - 20:50, 2 May 2016
  • Slecture template ECE662S14
    [[Category:pattern recognition]]
    1 KB (150 words) - 16:27, 30 April 2014
  • Data discussion HW1 ECE662 S14 Boutin
    [[Category:pattern recognition]]
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  • From Bayes Theorem to Pattern Recognition via Bayes Rule
    [[Category:pattern recognition]] '''From Bayes' Theorem to Pattern Recognition via Bayes' Rule''' <br />
    14 KB (2,241 words) - 10:42, 22 January 2015
  • Lecture 3 - Bayes Theorem and its Application to Pattern Recognition OldKiwi
    #REDIRECT [[From Bayes Theorem to Pattern Recognition via Bayes Rule]]
    70 B (10 words) - 07:10, 12 February 2014
  • Yelp Dataset
    [[Category:pattern recognition]]
    1 KB (212 words) - 03:38, 11 March 2014
  • Programming help ECE662S14
    [[Category:pattern recognition]]
    330 B (37 words) - 05:25, 17 February 2014
  • Upper Bounds for Bayes Error
    [1]. Duda, Richard O. and Hart, Peter E. and Stork, David G., "Pattern Classication (2nd Edition)," Wiley-Interscience, 2000. ...eering.purdue.edu/~mboutin/ Mireille Boutin], "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014.
    13 KB (2,062 words) - 10:45, 22 January 2015
  • Bayes Rule for 1-dimensional and N-dimensional feature spaces
    [[Category:pattern recognition]] ...widely used for many different kinds of applications, especially, pattern recognition. Due to its simplicity and effectiveness, we can use the method in both dis
    19 KB (3,255 words) - 10:47, 22 January 2015
  • SlectureKeehwanECE662Spring14
    [[Category:pattern recognition]] *Mireille Boutin, "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014.
    3 KB (427 words) - 10:50, 22 January 2015
  • PCA
    ...principle components. PCA has found numerous application fields like face recognition, dimension reduction, factor analysis and image compression, etc. Generally ...ed. More importantly, the direction of the eignevector shows a significant pattern in the data set. It's easy to find out that the blue line goes through all
    22 KB (3,459 words) - 10:40, 22 January 2015
  • ECE662 Whitening and Coloring Transforms S14 MH
    [[Category:pattern recognition]] ...eering.purdue.edu/~mboutin/ Mireille Boutin], "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014.
    17 KB (2,603 words) - 10:38, 22 January 2015
  • PCA comments
    [[Category:pattern recognition]]
    852 B (120 words) - 16:32, 14 May 2014
  • Mle tutorial
    [[Category:pattern recognition]] ...avid G. Stork, Pattern Classification. <br>3. Mimi Boutin, ECE 662 Pattern Recognition Lectures. <br>4. http://people.missouristate.edu/songfengzheng/Teaching/MTH
    25 KB (4,187 words) - 10:49, 22 January 2015
  • Slecture template video ECE662S14
    [[Category:pattern recognition]]
    1 KB (211 words) - 05:50, 3 September 2014
  • How to generate random n dimensional data from two categories with different priors slecture Minwoong Kim ECE662 Spring 2014
    [[Category:pattern recognition]]
    970 B (139 words) - 10:39, 22 January 2015
  • How to generate random data two classes Minwoong Kim review
    [[Category:pattern recognition]]
    3 KB (508 words) - 16:12, 14 May 2014
  • Slecture Bayes rule to minimize risk Andy Park ECE662 Spring 2014
    [[Category:pattern recognition]]
    2 KB (226 words) - 10:45, 22 January 2015
  • Bayes Rule Minimize Risk Dennis Lee
    [[Category:pattern recognition]] [1]. K. Fukunaga, ''Introduction to Statistical Pattern Recognition'' (Academic, New York, 1972).
    12 KB (1,810 words) - 10:46, 22 January 2015
  • Introduction to local density estimation methods
    [[Category:pattern recognition]] [1] Mireille Boutin, “ECE662: Statistical Pattern Recognition and Decision Making Processes,” Purdue University, Spring 2014<br>[2] htt
    15 KB (2,345 words) - 10:52, 22 January 2015
  • Curse of Dimensionality
    [[Category:pattern recognition]] ...ectly use all the pixel intensities in an image as the input to their face recognition algorithm. It's simply because there is too much redundant information in t
    9 KB (1,419 words) - 10:41, 22 January 2015
  • Bayes Parameter Estimation
    [[Category:pattern recognition]] [1] Mireille Boutin, "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014.<br>[2] Carl
    15 KB (2,273 words) - 10:51, 22 January 2015
  • Bayersian Parameter Estimation
    ...eering.purdue.edu/~mboutin/ Mireille Boutin], "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014. [2]. R. Duda, P. Hart, ''Pattern Classification''. Wiley-Interscience. Second Edition, 2000.
    8 KB (1,268 words) - 08:31, 29 April 2014
  • Slecture Neyman-Pearson Lemma and Receiver Operating Characteristic Curve ECE662Spring2014
    [[Category:pattern recognition]]
    1 KB (143 words) - 05:55, 23 April 2014
  • KnnDensityEstimation
    [[Category:pattern recognition]] ...eering.purdue.edu/~mboutin/ Mireille Boutin], "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014.<br/>[2] htt
    10 KB (1,743 words) - 10:54, 22 January 2015
  • Curse of Dimensionality Chinese
    [[Category:pattern recognition]]
    9 KB (238 words) - 10:41, 22 January 2015
  • Neyman-Pearson Lemma and Receiver Operating Characteristic Curve
    [[Category:pattern recognition]] [3] M. Boutin, "ECE662: Statistical Pattern Recognition and Decision Making Process," Spring 2014, Purdue University. <br>
    15 KB (2,306 words) - 10:48, 22 January 2015
  • Discussion about Discriminant Functions for the Multivariate Normal Density
    [[Category:Pattern recognition]] ...han other various density functions that have been investigated in pattern recognition.
    14 KB (2,287 words) - 10:46, 22 January 2015
  • Maximum Likelihood Estimation Analysis for various Probability Distributions
    [[Category:pattern recognition]] In the course, Purdue ECE 662, Pattern Recognition and Decision Taking Processes, we have already looked at the Maximum Likeli
    12 KB (1,986 words) - 10:49, 22 January 2015
  • Estimation Using Nearest Neighbor
    [[Category:pattern recognition]] ...aracteristics. The representative of real application such as body posture recognition using Procrustes metric could be a good example to understand the nearest n
    14 KB (2,313 words) - 10:55, 22 January 2015
  • Derivation of Bayes rule In Greek
    [[Category:pattern recognition]] Bishop C.M., 2006. “Pattern Recognition and Machine Learning”, Springer
    18 KB (665 words) - 10:43, 22 January 2015
  • ParzenWindow
    [[Category:pattern recognition]] [1] Pattern classification. Richard O. Duda, Peter E. Hart, and David G. Stork.<br>
    11 KB (1,824 words) - 10:53, 22 January 2015
  • 662slecture tang
    [[Category:pattern recognition]]
    9 KB (1,382 words) - 10:47, 22 January 2015
  • Bayersian Parameter Estimation: Gaussian Case
    ...eering.purdue.edu/~mboutin/ Mireille Boutin], "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014. [2]. R. Duda, P. Hart, ''Pattern Classification''. Wiley-Interscience. Second Edition, 2000.
    10 KB (1,625 words) - 10:51, 22 January 2015
  • Parzen Window Density Estimation
    [[Category:pattern recognition]] So far in our study of pattern recognition and classification we have primarily focused on the use of discriminant fun
    16 KB (2,703 words) - 10:54, 22 January 2015
  • Slecture Introduction local density estimation methods ECE662 Spring2014 Aziza
    ...ing 2014 lecture material of Prof. Mireille Boutin]] and Bishop's "Pattern Recognition and Machine Learning" Book . * Bishop C. (2006), "Pattern Recognition and Machine Learning", Springer-Verlag New York, Inc. Secaucus, NJ, USA.
    1 KB (171 words) - 10:52, 22 January 2015
  • PCA Theory Examples
    ...a representation are introduced. In the first example, 2D data of circular pattern is analyzed using PCA. Figure 8 shows the original circualr 2D data, and Fi <center>Figure 8. Circular pattern 2D data. </center>
    13 KB (1,990 words) - 11:42, 21 May 2014
  • ECE662 S14 Statistical Pattern recognition slectures collective
    [[Category:pattern recognition]] '''The Boutin Lectures on Statistical Pattern Recognition and Decision Making Processes, Spring 2014'''
    562 B (67 words) - 10:18, 29 April 2014
  • Least Squares Support Vector Machine and its Applications in Solving Linear Regression Problems
    ...Decision Making Processes," Purdue University, Spring 2014.<br>[2].&nbsp;"Pattern Classification" by Duda, Hart, and Stork
    14 KB (2,241 words) - 10:56, 22 January 2015
  • Derivation of Bayes rule In Chinese
    ...eering.purdue.edu/~mboutin/ Mireille Boutin], "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014.
    6 KB (535 words) - 10:43, 22 January 2015
  • ECE662 roc
    [[Category:pattern recognition]] ...A, USA. <br> [3] Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification. Wiley-Interscience. <br> [4] Detection Theory. http://www.e
    11 KB (1,823 words) - 10:48, 22 January 2015
  • Generation-of-normally-distributed-random-numbers-under-a-binary-prior-probability
    In the most of pattern recognition, decision and classification problems, the concept of training becomes popu
    16 KB (2,400 words) - 23:34, 29 April 2014
  • Kernel PCA
    [[Category:pattern recognition]]
    1 KB (161 words) - 10:41, 22 January 2015
  • How to generate random n dimensional data from two categories with different priors slecture Minwoong Cho ECE662 Spring 2014
    [[Category:pattern recognition]]
    967 B (131 words) - 10:39, 22 January 2015
  • SlectureAalshaiECE662Spring2014
    [[Category:pattern recognition]]
    996 B (137 words) - 07:53, 30 April 2014
  • Parzen Windows
    [[Category:pattern recognition]] [1] R.O. Duda, P.E. Hart, and D.G. Stork: Pattern Classification, Wiley, New York, NY, 2001
    12 KB (2,086 words) - 10:54, 22 January 2015
  • CBR logistic regression
    [[Category:pattern recognition]] [1] Mireille Boutin, “ECE662: Statistical Pattern Recognition and Decision Making Processes,” Purdue University, Spring 2014<br>
    9 KB (1,540 words) - 10:56, 22 January 2015
  • SlectureKeehwanECE662Spring14Review
    [[Category:pattern recognition]]
    1 KB (184 words) - 11:36, 2 May 2014
  • Derivation of Bayes rule Anonymous7
    [[Category:pattern recognition]] # Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification (2nd Edition). Wiley-Interscience.
    7 KB (1,106 words) - 10:42, 22 January 2015
  • Generating N-D Gaussian Data in Two Classes HyunDok Cho review
    [[Category:pattern recognition]]
    3 KB (490 words) - 16:21, 14 May 2014
  • Bayes Parameter Estimation with examples
    ...eering.purdue.edu/~mboutin/ Mireille Boutin], "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014.
    10 KB (1,600 words) - 10:52, 22 January 2015
  • Generation of N-dimensional normally distributed random numbers from two categories with different priors
    In the most of pattern recognition, decision and classification problems, the concept of training becomes popu
    18 KB (2,852 words) - 10:40, 22 January 2015
  • Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces Review
    [[Category:pattern recognition]]
    2 KB (359 words) - 09:58, 3 May 2014
  • Maximum Likelihood Estimators and Examples
    ...eering.purdue.edu/~mboutin/ Mireille Boutin], "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014.
    19 KB (3,418 words) - 10:50, 22 January 2015
  • Knearestneighbors
    [[Category:pattern recognition]]
    9 KB (1,604 words) - 10:54, 22 January 2015
  • Slecture from KNN to nearest neighbor
    [1] Lecture notes from ECE662: Statistical Pattern Recognition and Decision Making Processes, Purdue University, Mireille Boutin [2] Introduction to Statistical Pattern Recognition, 2nd edition, 1990, by K. Fukunaga.
    6 KB (1,013 words) - 10:55, 22 January 2015
  • Slecture kernelPCA of slecture review
    [[Category:pattern recognition]]
    695 B (104 words) - 16:37, 14 May 2014
  • The principles for how to generate random samples from a Gaussian distribution
    [[Category:pattern recognition]]
    8 KB (1,189 words) - 10:39, 22 January 2015
  • NearestNeighbor
    [[Category:pattern recognition]] ...aracteristics. The representative of real application such as body posture recognition using Procrustes metric could be a good example to understand the nearest n
    14 KB (2,323 words) - 04:54, 1 May 2014
  • NNM
    [[Category:pattern recognition]] ...aracteristics. The representative of real application such as body posture recognition using Procrustes metric could be a good example to understand the nearest n
    14 KB (2,340 words) - 17:24, 12 May 2014
  • Generating random data with controlled prior probabilities slecture ECE662S14 Gheith
    [[Category:pattern recognition]]
    1 KB (178 words) - 10:39, 22 January 2015
  • Introduction to local density estimation methods ECE662 Spring2014 Aziza
    [[Category:pattern recognition]]
    744 B (92 words) - 16:50, 4 May 2014
  • Reviews on Neyman-Pearson Lemma and Receiver Operating Characteristic Curve
    b) To motivate the problem better, since this comes under the pattern recognition course, the author could have spent a little more time discussing about why
    3 KB (425 words) - 23:42, 8 May 2014
  • K-Nearest Neighbors Density Estimation
    [[Category:pattern recognition]]
    931 B (124 words) - 10:55, 22 January 2015
  • Support Vector Machine
    * Bishop's "Pattern Recognition and Machine Learning" Book
    1 KB (158 words) - 10:56, 22 January 2015
  • Ness slecture 2014
    ...eering.purdue.edu/~mboutin/ Mireille Boutin], "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014. #Fukunaga, Keinosuke. Introduction to statistical pattern recognition. Academic press, 1990.
    10 KB (793 words) - 10:46, 22 January 2015
  • Introduction to local density estimation methods ECE662 Spring2014 Nusaybah
    [[Category:pattern recognition]]
    893 B (112 words) - 10:53, 22 January 2015
  • QA Nusaybah Abumulaweh ECEslecture Intro to Nonparametric density estimation
    ...gestion to bring up is that density estimation is a key element in pattern recognition, which is the main application of the course for which this slecture has be
    2 KB (263 words) - 05:27, 9 May 2014
  • SVMAndApplications
    ...us-one approach. You can find related materials in Bishop’s book Pattern Recognition and Machine Learning, in section 7.1.3 Multiclass SVMs.
    3 KB (510 words) - 19:07, 5 May 2014
  • Sample code enerating random data ECE662S14 slecture gheith
    [[Category:pattern recognition]] % ECE662: Pattern Recognition & Decision Making Processes
    5 KB (459 words) - 15:55, 14 May 2014
  • LinearClassierSlectureJMS
    1. Mirielle Boutin, “Lectures Notes from ECE662: Statistical Pattern Recognition and Decision Making Processes.” Purdue University, Spring 2014.
    10 KB (1,684 words) - 13:00, 5 May 2014
  • JMSLinearClassifierSlecture
    1. Mirielle Boutin, “Lectures Notes from ECE662: Statistical Pattern Recognition and Decision Making Processes.” Purdue University, Spring 2014.
    10 KB (1,666 words) - 10:56, 22 January 2015
  • SlectureDavidRunyanCS662Spring14
    [[Category:pattern recognition]] [1] Pattern Recognition and Machine Learning, 2006, by Christopher M. Bishop
    8 KB (1,350 words) - 10:57, 22 January 2015
  • 2014 Spring ECE 662 Boutin Statistical Pattern recognition slectures
    [[Category:pattern recognition]] '''The [http://mireilleboutin.com Boutin] Lectures on Statistical Pattern Recognition'''
    8 KB (1,123 words) - 10:38, 22 January 2015
  • Generating random data with controlled priorECE662S14 gheith review
    [[Category:pattern recognition]]
    1 KB (227 words) - 16:12, 14 May 2014
  • Hw1 Bochum Su2014
    [[Category:pattern recognitionBochumSummer2014Boutin]] ...ork 1, [[2014_Summer_pattern_recognition_Bochum_Boutin|Statistical Pattern Recognition, Summer 2014 (Bochum)]]=
    2 KB (245 words) - 01:57, 8 July 2014
  • Hw2 Bochum Su2014
    [[Category:pattern recognitionBochumSummer2014Boutin]] ...ork 2, [[2014_Summer_pattern_recognition_Bochum_Boutin|Statistical Pattern Recognition, Summer 2014 (Bochum)]]=
    2 KB (248 words) - 03:16, 8 July 2014
  • 2016 Spring ECE 662 Boutin
    == [[ECE662]]: '''Statistical Pattern Recognition and Decision Making Processes, Spring 2016''' (cross-listed with CS662) ==
    2 KB (270 words) - 19:10, 31 March 2016
  • Project1 ECE662S16
    [[Category:pattern recognition]]
    3 KB (434 words) - 17:19, 1 February 2016
  • Project2 ECE662S16
    [[Category:pattern recognition]]
    1 KB (238 words) - 13:32, 26 February 2016
  • Project3 ECE662S16
    [[Category:pattern recognition]]
    2 KB (302 words) - 19:11, 31 March 2016
  • Introduction of Optical Character Recognition (ORC)
    <center><font size= 4>Introduction to Optical Character Recognition(OCR)</font size> ...ing, assistive technology for blind and visually impaired users , zip-code recognition needed for post offices and much more.
    8 KB (1,405 words) - 22:56, 27 November 2016
  • Signals, Systems, and Biomedical Engineering
    ...ignal-generating processes and systems; analysis of nonstationary signals; pattern classification and diagnostic decision. MATLAB will be used throughout to p ...design fingerprint recognition, face recognition, iris recognition, voice recognition, and multimodal biometric systems. Students have hands-on experience in des
    12 KB (1,702 words) - 20:48, 9 April 2018

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