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  • OutlineECE662S10
    6. Parametric Density Estimation *Maximum likelihood estimation
    1 KB (165 words) - 08:55, 22 April 2010
  • Hw2 ECE662Spring2010
    Experiment with making decisions using Bayes rule and parametric density estimation. Summarize your experiments, results, and conclusions in a report (pdf). Ma *Discuss how the error in the density estimate affects the error in the decision.
    849 B (115 words) - 15:33, 10 May 2010
  • ECE662 hw3 discussions
    =Non-parametric density estimation in R= ...you might find these functions of interest for the non-parametric density estimation:
    3 KB (449 words) - 16:24, 9 May 2010
  • Hw3 ECE662Spring2010
    Experiment with making decisions using Bayes rule and non-parametric density estimation. Summarize your experiments, results, and conclusions in a report (pdf). Ma
    904 B (122 words) - 15:16, 10 May 2010
  • Lecture11ECE662S10
    ...ametric Density Estimation techniques. We discussed the Maximum Likelihood Estimation (MLE) method and look at a couple of 1-dimension examples for case when fea
    833 B (115 words) - 09:15, 11 May 2010
  • Decision Trees
    ...on using Series Expansion and Decision Trees_OldKiwi|Lecture 20 on Density Estimation using Series Expansion and Decision Trees]] *[[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_OldKiwi|Students notes for Lectur
    1 KB (164 words) - 10:10, 27 April 2010
  • Snikitha ListECE302
    ...al distributions. Estimation of means, variances. Correlation and spectral density functions. Random processes and response of linear systems to random inputs ...ependence, Cumulative Distribution Function (used in ECE 438), Probability Density Function (used in ECE 438), Probability Mass Function, functions of random
    2 KB (231 words) - 07:20, 4 May 2010
  • Decision Theory Glossary ECE662Spring2008
    == [[Bayesian Parameter Estimation_Old Kiwi|Bayesian Parameter Estimation]] == Bayesian Parameter Estimation is a technique for parameter estimation which uses probability densities as estimates of the parameters instead of
    31 KB (4,787 words) - 18:21, 22 October 2010
  • SA1050
    * [[Density Estimation]]
    1 KB (164 words) - 06:47, 18 November 2010
  • ECE 600 Prerequisites
    ...sity Function)|CDF (Cumulative Distribution Function) and PDF (Probability Density Function)]] ...erequisites Minimum Mean-Square Error Estimation|Minimum Mean-Square Error Estimation]]
    1 KB (139 words) - 13:13, 16 November 2010
  • KNN-K Nearest Neighbor OldKiwi
    ...time, but it also has disadvantage - memory intensive, classification and estimation are slow. ...rest_Neighbor_Density_Estimate_Old_Kiwi|Lecture 16: Parzen Windows and KNN density estimates]]
    2 KB (253 words) - 07:35, 1 December 2010
  • Lecture 1 - Introduction OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    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 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    8 KB (1,299 words) - 11:24, 10 June 2013
  • Lecture14ECE662S12
    [[Category:maximum likelihood estimation]] *[[Parametric_Estimators_OldKiwi|A student page about parametric density estimation, from ECE662 Spring 2008]]
    2 KB (319 words) - 13:27, 8 March 2012
  • Lecture15ECE662S12
    [[Category:bayesian parameter estimation]] ...he use of Bayesian Parameter Estimation for estimating the parameters of a density.
    1 KB (172 words) - 12:27, 6 March 2012
  • Lecture16ECE662S12
    [[Category:density estimation]] ...at region, and the total number of samples) for estimating the probability density function at a point x of the feature space.
    2 KB (205 words) - 12:33, 6 March 2012
  • Lecture17ECE662S12
    [[Category:density estimation]] Today we discussed the Parzen window method for estimating the probability density function at a point x of the feature space using samples drawn.
    2 KB (204 words) - 13:56, 8 March 2012
  • Hw2 ECE662 S12
    [[Category:density estimation]] ...e context of a decision problem, or you can compare them solely as density estimation techniques. Summarize your experiments, results, and conclusions in a repor
    1 KB (164 words) - 14:25, 30 May 2012
  • Parametric Estimators OldKiwi
    ...ese methods are Maximum Likelihood Estimation (MLE) and Bayesian parameter estimation. Despite the difference in theory between these two methods, they are quit ==Comparison of MLE and Bayesian Parameter Estimation==
    6 KB (976 words) - 13:25, 8 March 2012
  • Lecture18ECE662S12
    [[Category:density estimation]] ...hed discussing the the Parzen window method for estimating the probability density function at a point x of the feature space using samples. In particular, we
    2 KB (287 words) - 10:34, 22 March 2012
  • Lecture19ECE662S12
    [[Category:density estimation]] ...ty, and we showed that it is an unbiased estimate of the true value of the density at that point. We also showed how this formula is the basis for using the "
    2 KB (274 words) - 10:34, 22 March 2012
  • Lecture 26 - Statistical Clustering Methods OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    8 KB (1,214 words) - 11:24, 10 June 2013
  • Lecture 27 - Clustering by finding valleys of densities OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    8 KB (1,313 words) - 11:24, 10 June 2013
  • Lecture 28 - Final lecture OldKiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]|
    10 KB (1,704 words) - 11:25, 10 June 2013
  • Lecture20ECE662S12
    [[Category:density estimation]] ...the nearest neighbor among a set of labeled samples drawn from the mixture density.
    2 KB (269 words) - 03:40, 12 April 2012
  • HW2 peer review ECE662S12
    ...problem/question investigated concerned with a relevant aspect of "density estimation techniques"? Is the problem/question addressed clearly stated? Is the probl
    2 KB (375 words) - 12:31, 9 April 2012
  • 2013 Spring ECE 302 Boutin
    ...tion_find_conditional_pdf_ECE302S13Boutin|Find the conditional probability density function]] ..._find_conditional_ellipse_ECE302S13Boutin|Find the conditional probability density function (again)]]
    10 KB (1,422 words) - 20:14, 30 April 2013
  • ECE662 Pattern Recognition Decision Making Processes Spring2008 sLecture collective
    ...timation (Parzen Window)_OldKiwi|Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)]] ...Estimate_OldKiwi|Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate]]
    3 KB (425 words) - 09:59, 4 November 2013
  • KNN Algorithm OldKiwi
    The non-parametric density estimation is *With enough samples we can converge to an target density
    5 KB (833 words) - 03:31, 19 April 2013

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

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