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  • 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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