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Page title matches

  • Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    10 KB (1,607 words) - 08:38, 17 January 2013
  • Lecture 19 - Nearest Neighbor Error Rates Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    5 KB (1,003 words) - 08:40, 17 January 2013
  • KNN-K Nearest Neighbor Old Kiwi
    =K Nearest Neighbors= #*How K-Nearest Neighbor (KNN) Algorithm works?
    1 KB (170 words) - 17:56, 22 October 2010
  • KNN-K Nearest Neighbor OldKiwi
    =K Nearest Neighbors (KNN)= K nearest neighbor (KNN) classifiers do not use any model to fit the data and only based on me
    2 KB (253 words) - 07:35, 1 December 2010
  • Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    10 KB (1,609 words) - 11:22, 10 June 2013
  • Lecture 19 - Nearest Neighbor Error Rates OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    6 KB (1,041 words) - 11:22, 10 June 2013
  • Estimation Using Nearest Neighbor
    Nearest Neighbor Method ...using Procrustes metric could be a good example to understand the nearest neighbor rule.----
    14 KB (2,313 words) - 10:55, 22 January 2015
  • Slecture from KNN to nearest neighbor
    <font size="4">From KNN to Nearest Neighbor Classification </font> ...s tutorial, we will explain first the concept of KNN, secondly the nearest neighbor approach, and thirdly discuss briefly the comparative advantages and disadv
    6 KB (1,013 words) - 10:55, 22 January 2015
  • Slecture KNN to nearest neighbor
    294 B (33 words) - 16:13, 30 April 2014
  • KNN to nearest neighbor slecture review comment
    ...size="4">Review on KNN to [[Slecture_from_KNN_to_nearest_neighbor|Nearest Neighbor Slecture by Jonathan Manring]] </font> ...on method and transitions from KNN into a brief description of the nearest neighbor classification method. A few comments/suggestions:
    2 KB (284 words) - 11:20, 7 May 2014

Page text matches

  • ECE662:BoutinSpring08 Old Kiwi
    * [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi]] * [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi]]
    6 KB (747 words) - 05:18, 5 April 2013
  • ECE662:Glossary Old Kiwi
    ...s include logistic regression, generalized linear classifiers, and nearest-neighbor. See "Discriminative and Learning". == [[KNN-K Nearest Neighbor_Old Kiwi|KNN-K Nearest Neighbor]] ==
    31 KB (4,832 words) - 18:13, 22 October 2010
  • Homework 2 Old Kiwi
    b) Design a classifier using the K-nearest neighbor technique c) Design a classifier using the nearest neighbor technique.
    5 KB (746 words) - 16:33, 17 April 2008
  • Lecture 17 - Nearest Neighbors Clarification Rule and Metrics Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    6 KB (938 words) - 08:38, 17 January 2013
  • Lecture 1 - Introduction Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    3 KB (468 words) - 08:45, 17 January 2013
  • Lecture 2 - Decision Hypersurfaces Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    5 KB (737 words) - 08:45, 17 January 2013
  • Lecture 3 - Bayes classification Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    5 KB (843 words) - 08:46, 17 January 2013
  • Lecture 5 - Discriminant Functions Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    6 KB (916 words) - 08:47, 17 January 2013
  • Lecture 6 - Discriminant Functions Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    9 KB (1,586 words) - 08:47, 17 January 2013
  • Lecture 7 - MLE and BPE Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    10 KB (1,488 words) - 10:16, 20 May 2013
  • Lecture 8 - MLE, BPE and Linear Discriminant Functions Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    5 KB (792 words) - 08:48, 17 January 2013
  • Lecture 9 - Linear Discriminant Functions Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    8 KB (1,307 words) - 08:48, 17 January 2013
  • Lecture 10 - Batch Perceptron and Fisher Linear Discriminant Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    5 KB (755 words) - 08:48, 17 January 2013
  • Lecture 11 - Fischer's Linear Discriminant again Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    5 KB (907 words) - 08:49, 17 January 2013
  • Lecture 12 - Support Vector Machine and Quadratic Optimization Problem Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    8 KB (1,235 words) - 08:49, 17 January 2013
  • Lecture 13 - Kernel function for SVMs and ANNs introduction Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    8 KB (1,354 words) - 08:51, 17 January 2013
  • Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    13 KB (2,073 words) - 08:39, 17 January 2013
  • Lecture 15 - Parzen Window Method Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    7 KB (1,212 words) - 08:38, 17 January 2013
  • Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    10 KB (1,607 words) - 08:38, 17 January 2013
  • Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    6 KB (1,066 words) - 08:40, 17 January 2013
  • ECE662:ChangeLog Old Kiwi
    ...tance (ED)_Old Kiwi]], [[Generalized Rayleigh Quotient_Old Kiwi]], [[KNN-K Nearest Neighbor_Old Kiwi]], [[Partial Differential Equations (PDE)_Old Kiwi]], [[P .../04/26 -- Added part of an equation and bayes error rate graph for Nearest Neighbor in Lecture 19.
    10 KB (1,418 words) - 12:21, 28 April 2008
  • KNN algorithm Old Kiwi
    == [[KNN-K_Nearest_Neighbor_Old_Kiwi|k-Nearest Neighbor]] Algorithm == ...rs ought to be the class that the sample belongs to. The so called Nearest Neighbor algorithm is the particular instance of the [[KNN-K_Nearest_Neighbor_Old_Ki
    3 KB (503 words) - 17:53, 22 October 2010
  • Publications Old Kiwi
    ===A 1967 paper introducing Nearest neighbor algorithm using the Bayes probability of error=== *'''T. Cover and P. Hart, "Nearest neighbor pattern classification", IEEE Transactions on Information Theory vol. 13, I
    39 KB (5,715 words) - 10:52, 25 April 2008
  • Probabilistic neural networks Old Kiwi
    PNNs work on a very similar principal as that of K-Nearest Neighbor (k-NN) models. The idea is that, a a predicted target value of an item is l
    2 KB (308 words) - 08:47, 10 April 2008
  • Lecture 4 - Bayes Classification Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    8 KB (1,360 words) - 08:46, 17 January 2013
  • Case-based Reasoning Old Kiwi
    ...research areas. One example of such external contributions is the nearest neighbor algorithm, NNA. For case-based reasoning, CBR, we use NNA as the backbone o
    6 KB (1,055 words) - 11:14, 7 April 2008
  • KNN Algorithm Old Kiwi
    '''KNN(K-Nearest Neighbor)''' == k-Nearest Neighbor (kNN) Algorithm ==
    4 KB (637 words) - 08:46, 10 April 2008
  • Lecture 19 - Nearest Neighbor Error Rates Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    5 KB (1,003 words) - 08:40, 17 January 2013
  • Lecture 20 - Density Estimation using Series Expansion and Decision Trees Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    6 KB (1,047 words) - 08:42, 17 January 2013
  • Classification Old Kiwi
    ...s include logistic regression, generalized linear classifiers, and nearest-neighbor. See "Discriminative and Learning".
    888 B (134 words) - 10:03, 31 March 2008
  • Lecture 21 - Decision Trees(Continued) Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    6 KB (1,012 words) - 08:42, 17 January 2013
  • Lecture 22 - Decision Trees and Clustering Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    6 KB (806 words) - 08:42, 17 January 2013
  • Editing technique Old Kiwi
    In the K Nearest Neighbor (K-NN) technique, we need to store n samples of the training set. If n is v ...ed sample will generate two homogeneous sets of samples with the a nearest neighbor decision boundary approximate the Bayes decision boundary (Fig. 2).
    2 KB (296 words) - 11:48, 7 April 2008
  • Lecture 23 - Spanning Trees Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    7 KB (1,060 words) - 08:43, 17 January 2013
  • Lecture 24 - Clustering and Hierarchical Clustering Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    8 KB (1,254 words) - 08:43, 17 January 2013
  • Lecture 25 - Clustering Algorithms Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    8 KB (1,259 words) - 08:43, 17 January 2013
  • Nonparametric regression/density estimation Old Kiwi
    ...ge amount of data. This includes histograms, kernel smoothing, and nearest-neighbor.
    185 B (26 words) - 01:42, 17 April 2008
  • Lecture 26 - Statistical Clustering Methods Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    8 KB (1,244 words) - 08:44, 17 January 2013
  • Leave-one-out Cross Validation Old Kiwi
    When applying K-nearest neighbor (KNN) method or Artifical Neural Network (ANN) method for classification, t
    1 KB (190 words) - 14:04, 17 April 2008
  • Lecture 27 - Clustering by finding valleys of densities Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    8 KB (1,337 words) - 08:44, 17 January 2013
  • Lecture 28 - Final lecture Old Kiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]],
    10 KB (1,728 words) - 08:55, 17 January 2013
  • KNN-K Nearest Neighbor Old Kiwi
    =K Nearest Neighbors= #*How K-Nearest Neighbor (KNN) Algorithm works?
    1 KB (170 words) - 17:56, 22 October 2010
  • Recommendations for nonlinear discrimination methods Old Kiwi
    #Try simple pattern recognition technique (K-nearest neighbor, linear discriminant analysis)first as a baseline before trying Neural netw
    2 KB (311 words) - 10:49, 26 April 2008
  • Lecture 2 - Decision Hypersurfaces OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    5 KB (744 words) - 11:17, 10 June 2013
  • ECE662:BoutinSpring08 OldKiwi
    ...r Density Estimate_OldKiwi|Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate]] ...17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|Lecture 17 - Nearest Neighbors Clarification Rule and Metrics]]
    7 KB (875 words) - 07:11, 13 February 2012
  • ECE662
    *[[KNN-K_Nearest_Neighbor_OldKiwi|The K Nearest Neighbor Algorithm]]
    3 KB (429 words) - 09:07, 11 January 2016
  • Lecture 9 - Linear Discriminant Functions OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    9 KB (1,341 words) - 11:15, 10 June 2013
  • OutlineECE662S10
    *K-nearest neighbors *The nearest neighbor classification rule.
    1 KB (165 words) - 08:55, 22 April 2010
  • ECE662 hw3 discussions
    ...central/fileexchange/15562-k-nearest-neighbors function] for finding the k-nearest neighbors (kNN) within a set of points, which could be useful for homework ...veling Salesman Problem" (TSP) is one of the interesting applications of K-nearest neighbors (KNN) method. As you know, TSP is one of the most important probl
    3 KB (449 words) - 16:24, 9 May 2010
  • Hw3 ECE662Spring2010
    ...ferent techniques we learned (k-nearest neighbors, parzen windows, nearest neighbor).
    904 B (122 words) - 15:16, 10 May 2010
  • Homework 2 OldKiwi
    b) Design a classifier using the K-nearest neighbor technique c) Design a classifier using the nearest neighbor technique.
    5 KB (761 words) - 10:53, 13 April 2010
  • FavoritedecisionECE662S10
    *Nearest neighbors. It reminds me of human behavior in that if we don't know what t *Nearest neighbor. From practical point of view, it is easy to implement and quite fast (and,
    6 KB (884 words) - 16:26, 9 May 2010
  • HW3 KNNandNN comp zge
    [[Category:nearest neighbor]] [[Category:k nearest neighbors]]
    976 B (151 words) - 10:47, 22 March 2012
  • Decision Theory Glossary ECE662Spring2008
    ...s include logistic regression, generalized linear classifiers, and nearest-neighbor. See "Discriminative and Learning". == [[KNN-K Nearest Neighbor_Old Kiwi|KNN-K Nearest Neighbor]] ==
    31 KB (4,787 words) - 18:21, 22 October 2010
  • Machine learning tutorials
    *[[KNN-K_Nearest_Neighbor_OldKiwi|The K Nearest Neighbor Algorithm]]
    1 KB (156 words) - 12:26, 27 March 2015
  • KNN algorithm OldKiwi
    == k-Nearest Neighbor Algorithm == ...rs ought to be the class that the sample belongs to. The so called Nearest Neighbor algorithm is the particular instance of the kNN when k=1.
    3 KB (494 words) - 07:23, 1 December 2010
  • KNN-K Nearest Neighbor OldKiwi
    =K Nearest Neighbors (KNN)= K nearest neighbor (KNN) classifiers do not use any model to fit the data and only based on me
    2 KB (253 words) - 07:35, 1 December 2010
  • Lecture 1 - Introduction OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    3 KB (413 words) - 11:17, 10 June 2013
  • Lecture 3 - Bayes classification OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    6 KB (874 words) - 11:17, 10 June 2013
  • Lecture 4 - Bayes Classification OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    8 KB (1,403 words) - 11:17, 10 June 2013
  • Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    10 KB (1,609 words) - 11:22, 10 June 2013
  • Lecture 17 - Nearest Neighbors Clarification Rule and Metrics OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    6 KB (977 words) - 11:22, 10 June 2013
  • Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    7 KB (1,098 words) - 11:22, 10 June 2013
  • Lecture 6 - Discriminant Functions OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    10 KB (1,604 words) - 11:17, 10 June 2013
  • Lecture 7 - MLE and BPE OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    10 KB (1,472 words) - 11:16, 10 June 2013
  • Lecture 5 - Discriminant Functions OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    6 KB (946 words) - 11:17, 10 June 2013
  • Lecture 8 - MLE, BPE and Linear Discriminant Functions OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    6 KB (833 words) - 11:16, 10 June 2013
  • Lecture 10 - Batch Perceptron and Fisher Linear Discriminant OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    6 KB (813 words) - 11:18, 10 June 2013
  • Lecture 11 - Fischer's Linear Discriminant again OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    6 KB (946 words) - 11:18, 10 June 2013
  • Lecture 12 - Support Vector Machine and Quadratic Optimization Problem OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    8 KB (1,278 words) - 11:19, 10 June 2013
  • Lecture 13 - Kernel function for SVMs and ANNs introduction OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    9 KB (1,389 words) - 11:19, 10 June 2013
  • Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    13 KB (2,098 words) - 11:21, 10 June 2013
  • Lecture 15 - Parzen Window Method OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    8 KB (1,246 words) - 11:21, 10 June 2013
  • Lecture 19 - Nearest Neighbor Error Rates OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    6 KB (1,041 words) - 11:22, 10 June 2013
  • Lecture 20 - Density Estimation using Series Expansion and Decision Trees OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    7 KB (1,082 words) - 11:23, 10 June 2013
  • Lecture 21 - Decision Trees(Continued) OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    7 KB (1,055 words) - 11:23, 10 June 2013
  • Lecture 22 - Decision Trees and Clustering OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    6 KB (837 words) - 11:23, 10 June 2013
  • Lecture 23 - Spanning Trees OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    7 KB (1,091 words) - 11:23, 10 June 2013
  • Lecture 24 - Clustering and Hierarchical Clustering OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    9 KB (1,276 words) - 11:24, 10 June 2013
  • Lecture 25 - Clustering Algorithms OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    8 KB (1,299 words) - 11:24, 10 June 2013
  • Lecture19ECE662S12
    [[Category:k nearest neighbors]] ...rest neighbor (KNN) density estimation technique, along with the k-nearest neighbor (KNN) pattern recognition method. More specifically, we presented a formula
    2 KB (274 words) - 10:34, 22 March 2012
  • Lecture 26 - Statistical Clustering Methods OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    8 KB (1,214 words) - 11:24, 10 June 2013
  • Lecture 27 - Clustering by finding valleys of densities OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    8 KB (1,313 words) - 11:24, 10 June 2013
  • Lecture 28 - Final lecture OldKiwi
    [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]|
    10 KB (1,704 words) - 11:25, 10 June 2013
  • Lecture20ECE662S12
    [[Category:k nearest neighbors]] [[Category:nearest neighbor]]
    2 KB (269 words) - 03:40, 12 April 2012
  • Lecture21ECE662S12
    [[Category:k nearest neighbors]] [[Category:nearest neighbor]]
    2 KB (259 words) - 03:57, 12 April 2012
  • ECE662 Pattern Recognition Decision Making Processes Spring2008 sLecture collective
    ...r Density Estimate_OldKiwi|Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate]] ...17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|Lecture 17 - Nearest Neighbors Clarification Rule and Metrics]]
    3 KB (425 words) - 09:59, 4 November 2013
  • KNN Algorithm OldKiwi
    '''KNN(K-Nearest Neighbor)''' == k-Nearest Neighbor (kNN) Algorithm ==
    5 KB (833 words) - 03:31, 19 April 2013
  • 2014 Spring ECE 662 Boutin
    **Density Estimation with K-Nearest Neighbors (KNN) ***[[K-Nearest Neighbors Density Estimation|Video slecture in English]] by Qi Wang <span s
    10 KB (1,450 words) - 20:50, 2 May 2016
  • Bayes Rule for 1-dimensional and N-dimensional feature spaces
    ...). Thus, for a given data <math>x</math>, we can choose a class that has a nearest mean from <math>x</math>. Let's see the following derivation: ...Estimation, Bayesian Parameter Estimation, Parzen window method, k-nearest neighbor, and so on. One related and interesting problem needed to further investiga
    19 KB (3,255 words) - 10:47, 22 January 2015
  • Introduction to local density estimation methods
    ...nsity estimation methods which are Parzen density estimation and k-nearest neighbor density estimation. Local density estimation is also referred to as non-par == '''4. K-Nearest Neighbor Density Estimation''' ==
    15 KB (2,345 words) - 10:52, 22 January 2015
  • KnnDensityEstimation
    K-Nearest Neighbors Density Estimation This slecture discusses about the K-Nearest Neighbors(k-NN) approach to estimate the density of a given distribution.
    10 KB (1,743 words) - 10:54, 22 January 2015
  • Slecture Introduction to local density estimation methods review
    ...density estimation methods, Parzen window density estimation and K-nearest neighbor density estimation. The general principle of both of the two methods are ca
    2 KB (285 words) - 17:34, 2 May 2014
  • Estimation Using Nearest Neighbor
    Nearest Neighbor Method ...using Procrustes metric could be a good example to understand the nearest neighbor rule.----
    14 KB (2,313 words) - 10:55, 22 January 2015
  • NN Comment
    Questions and comments for [[Estimation_Using_Nearest_Neighbor|Nearest Neighbor Method]]. Back to [[NNM|Nearest Neighbor Method]].
    1 KB (203 words) - 19:12, 12 May 2014
  • Slecture from KNN to nearest neighbor
    <font size="4">From KNN to Nearest Neighbor Classification </font> ...s tutorial, we will explain first the concept of KNN, secondly the nearest neighbor approach, and thirdly discuss briefly the comparative advantages and disadv
    6 KB (1,013 words) - 10:55, 22 January 2015
  • KNN to nearest neighbor slecture review comment
    ...size="4">Review on KNN to [[Slecture_from_KNN_to_nearest_neighbor|Nearest Neighbor Slecture by Jonathan Manring]] </font> ...on method and transitions from KNN into a brief description of the nearest neighbor classification method. A few comments/suggestions:
    2 KB (284 words) - 11:20, 7 May 2014
  • NearestNeighbor
    Nearest Neighbor Method ...using Procrustes metric could be a good example to understand the nearest neighbor rule.----
    14 KB (2,323 words) - 04:54, 1 May 2014
  • NNM
    Nearest Neighbor Method ...using Procrustes metric could be a good example to understand the nearest neighbor rule.----
    14 KB (2,340 words) - 17:24, 12 May 2014
  • 2014 Spring ECE 662 Boutin Statistical Pattern recognition slectures
    *Density Estimation with K-Nearest Neighbors (KNN) **[[K-Nearest Neighbors Density Estimation|Video slecture in English]] by Qi Wang
    8 KB (1,123 words) - 10:38, 22 January 2015

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