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=[[Hw3_ECE662Spring2010|Homework 3]] discussion, [[ECE662]], Spring 2010, [[user:mboutin|Prof. Boutin]]=
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I found a MATLAB [http://www.mathworks.com/matlabcentral/fileexchange/15562-k-nearest-neighbors function] for finding the k-nearest neighbors (kNN) within a set of points, which could be useful for homework 3.
 
I found a MATLAB [http://www.mathworks.com/matlabcentral/fileexchange/15562-k-nearest-neighbors function] for finding the k-nearest neighbors (kNN) within a set of points, which could be useful for homework 3.
  
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--[[User:ilaguna|ilaguna]] 15:40, 7 April 2010 (UTC)
 
--[[User:ilaguna|ilaguna]] 15:40, 7 April 2010 (UTC)
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[[2010_Spring_ECE_662_mboutin|back to ECE662, Spring 2010, Prof. Boutin]]

Revision as of 09:56, 16 April 2010

Homework 3 discussion, ECE662, Spring 2010, Prof. Boutin

I found a MATLAB function for finding the k-nearest neighbors (kNN) within a set of points, which could be useful for homework 3.

I tried it and it works well. I did some experiments using the Wine data set of UCI (http://archive.ics.uci.edu/ml/datasets.html). I used attributes 1 and 7 of the red wine data set (red points) and the white wine data set (grey points). For this simple experiment, I used only the first 100 data points of each set. The following figures show the classification regions using k=1, 3, 7. The red wine region is brown and the white wine region is white. The regions are constructed using MATLAB's contourf function.

Nn k 1.jpg Nn k 3.jpg Nn k 7.jpg

--ilaguna 15:40, 7 April 2010 (UTC)


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