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  • ECE662:ChangeLog Old Kiwi
    ...quations in [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi]], so that all are now correctly displayed.
    10 KB (1,418 words) - 12:21, 28 April 2008
  • Lecture 4 - Bayes Classification Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    8 KB (1,360 words) - 08:46, 17 January 2013
  • Parzen Window Estimation example Old Kiwi
    ...s Parzen-window estimates of a univariate gaussian density using different window widths and number of samples. h1 = [1 0.6 0.15]; % this parameter controls the window width h_n
    2 KB (267 words) - 20:45, 26 March 2008
  • KNN Algorithm Old Kiwi
    '''Parzen window approach'''
    4 KB (637 words) - 08:46, 10 April 2008
  • Lecture 19 - Nearest Neighbor Error Rates Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    5 KB (1,003 words) - 08:40, 17 January 2013
  • Lecture 20 - Density Estimation using Series Expansion and Decision Trees Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    6 KB (1,047 words) - 08:42, 17 January 2013
  • Lecture 21 - Decision Trees(Continued) Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    6 KB (1,012 words) - 08:42, 17 January 2013
  • Lecture 22 - Decision Trees and Clustering Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    6 KB (806 words) - 08:42, 17 January 2013
  • Working Parzen Window Code in Scilab Old Kiwi
    // Scilab Parzen-Window Classifier code // Parameters h=(window size),
    2 KB (267 words) - 00:40, 7 April 2008
  • Lecture 23 - Spanning Trees Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    7 KB (1,060 words) - 08:43, 17 January 2013
  • Lecture 24 - Clustering and Hierarchical Clustering Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    8 KB (1,254 words) - 08:43, 17 January 2013
  • Lecture 25 - Clustering Algorithms Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    8 KB (1,259 words) - 08:43, 17 January 2013
  • Pazen Window Old Kiwi
    ...inges Thhe Parzen-window density estimate using n training samples and the window function tex: \pi is defined by ...imate <math>p_n(x)</math> is an average of (window) functions. Usually the window function has its maximum at the origin and its values become smaller when w
    1 KB (194 words) - 01:44, 17 April 2008
  • Parzen Window Old Kiwi
    ...inges Thhe Parzen-window density estimate using n training samples and the window function tex: \pi is defined by ...imate <math>p_n(x)</math> is an average of (window) functions. Usually the window function has its maximum at the origin and its values become smaller when w
    1 KB (194 words) - 01:54, 17 April 2008
  • Lecture 26 - Statistical Clustering Methods Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    8 KB (1,244 words) - 08:44, 17 January 2013
  • Lecture 27 - Clustering by finding valleys of densities Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    8 KB (1,337 words) - 08:44, 17 January 2013
  • Lecture 28 - Final lecture Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 15 - Parzen Window Method_Old Kiwi|15]],
    10 KB (1,728 words) - 08:55, 17 January 2013
  • Lecture 2 - Decision Hypersurfaces OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    5 KB (744 words) - 11:17, 10 June 2013
  • ECE662:BoutinSpring08 OldKiwi
    ...ndow)_OldKiwi|Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)]] * [[Lecture 15 - Parzen Window Method_OldKiwi|Lecture 15 - Parzen Window Method]]
    7 KB (875 words) - 07:11, 13 February 2012
  • Lecture 9 - Linear Discriminant Functions OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    9 KB (1,341 words) - 11:15, 10 June 2013
  • ECE662 hw3 discussions
    I haven't found a method for data classification using Parzen window method, but you can use some packages for kernel density estimation of mult ...use using parameter "ckertype". The size of the kernel (size of the Parzen window) can be changed by modifying the bandwith of the kernel (parameter "bws")
    3 KB (449 words) - 16:24, 9 May 2010
  • Hw3 ECE662Spring2010
    ...he accuracy of the 3 different techniques we learned (k-nearest neighbors, parzen windows, nearest neighbor). *Discuss how the choice of K, or the parzen window size, affects your results.
    904 B (122 words) - 15:16, 10 May 2010
  • Homework 2 OldKiwi
    a) Design a classifier using the Parzen window technique. c). Demonstration of parzen window
    5 KB (761 words) - 10:53, 13 April 2010
  • Decision Theory Glossary ECE662Spring2008
    == [[Parzen Window_Old Kiwi|Parzen Window]] == ...inges Thhe Parzen-window density estimate using n training samples and the window function tex: \pi is defined by
    31 KB (4,787 words) - 18:21, 22 October 2010
  • Lecture 1 - Introduction OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    3 KB (413 words) - 11:17, 10 June 2013
  • Lecture 3 - Bayes classification OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    6 KB (874 words) - 11:17, 10 June 2013
  • Lecture 4 - Bayes Classification OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    8 KB (1,403 words) - 11:17, 10 June 2013
  • Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    10 KB (1,609 words) - 11:22, 10 June 2013
  • Lecture 17 - Nearest Neighbors Clarification Rule and Metrics OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    6 KB (977 words) - 11:22, 10 June 2013
  • Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    7 KB (1,098 words) - 11:22, 10 June 2013
  • Lecture 6 - Discriminant Functions OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    10 KB (1,604 words) - 11:17, 10 June 2013
  • Lecture 7 - MLE and BPE OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    10 KB (1,472 words) - 11:16, 10 June 2013
  • Lecture 5 - Discriminant Functions OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    6 KB (946 words) - 11:17, 10 June 2013
  • Lecture 8 - MLE, BPE and Linear Discriminant Functions OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    6 KB (833 words) - 11:16, 10 June 2013
  • Lecture 10 - Batch Perceptron and Fisher Linear Discriminant OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    6 KB (813 words) - 11:18, 10 June 2013
  • Lecture 11 - Fischer's Linear Discriminant again OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    6 KB (946 words) - 11:18, 10 June 2013
  • Lecture 12 - Support Vector Machine and Quadratic Optimization Problem OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    8 KB (1,278 words) - 11:19, 10 June 2013
  • Lecture 13 - Kernel function for SVMs and ANNs introduction OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    9 KB (1,389 words) - 11:19, 10 June 2013
  • Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    13 KB (2,098 words) - 11:21, 10 June 2013
  • Lecture 15 - Parzen Window Method OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    8 KB (1,246 words) - 11:21, 10 June 2013
  • Lecture 19 - Nearest Neighbor Error Rates OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    6 KB (1,041 words) - 11:22, 10 June 2013
  • Lecture 20 - Density Estimation using Series Expansion and Decision Trees OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    7 KB (1,082 words) - 11:23, 10 June 2013
  • Lecture 21 - Decision Trees(Continued) OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    7 KB (1,055 words) - 11:23, 10 June 2013
  • Lecture 22 - Decision Trees and Clustering OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    6 KB (837 words) - 11:23, 10 June 2013
  • Lecture 23 - Spanning Trees OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    7 KB (1,091 words) - 11:23, 10 June 2013
  • Lecture 24 - Clustering and Hierarchical Clustering OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    9 KB (1,276 words) - 11:24, 10 June 2013
  • Lecture 25 - Clustering Algorithms OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    8 KB (1,299 words) - 11:24, 10 June 2013
  • Lecture17ECE662S12
    [[Category:parzen window]] Today we discussed the Parzen window method for estimating the probability density function at a point x of the
    2 KB (204 words) - 13:56, 8 March 2012
  • Lecture18ECE662S12
    [[Category:parzen window]] ...the neighboring samples. However, it was pointed out that using different window volumes for different classes might improve the result of this voting proce
    2 KB (287 words) - 10:34, 22 March 2012
  • Lecture 26 - Statistical Clustering Methods OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 15 - Parzen Window Method_OldKiwi|15]]|
    8 KB (1,214 words) - 11:24, 10 June 2013

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Ph.D. 2007, working on developing cool imaging technologies for digital cameras, camera phones, and video surveillance cameras.

Buyue Zhang