Code covered by the BSD License  

Highlights from
4-Nearest Neighbor on iris recognition using randomized partitioning.

4.0

4.0 | 1 rating Rate this file 24 Downloads (last 30 days) File Size: 2.04 KB File ID: #37827
image thumbnail

4-Nearest Neighbor on iris recognition using randomized partitioning.

by

 

Matlab Script to find the 4 - nearest neighbors (kNN) for IRIS dataset

| Watch this File

File Information
Description

% 1: Load iris.mat file which contains Iris data and its label
% seperately.
% 2: Randomize the order of data for each iternation so that new sets of
% training and test data are formed.
%
% The training data is of having size of Nxd where N is the number of
% measurements and d is the number of variables of the training data.
%
% Similarly the size of the test data is Mxd where M is the number of
% measurements and d is the number of variables of the test data.

% 3: For each observation in test data, we compute the euclidean distance
% from each obeservation in training data.
% 4: We evalutate 'k' nearest neighbours among them and store it in an
% array.
% 5: We apply the label for which distance is minimum
% 5.1: In case of a tie, we randomly label the class.
% 6: Return the class label.
% 7: Compute confusion matrix.

Acknowledgements

K Nearest Neighbors inspired this file.

Required Products Simulink Verification and Validation
MATLAB release MATLAB 7.14 (R2012a)
Tags for This File   Please login to tag files.
Please login to add a comment or rating.
Comments and Ratings (1)
01 Apr 2014 Mohamed

It is well documented, but iris.mat is not included !

Contact us