kNN classifier

k-NN classifier
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Updated 4 Jan 2019

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-k-NN classifier: classifying using k-nearest neighbors algorithm. The nearest neighbors
-search method is euclidean distance
-Usage:
[predicted_labels,nn_index,accuracy] = KNN_(3,training,training_labels,testing,testing_labels)
predicted_labels = KNN_(3,training,training_labels,testing)
-Input:
- k: number of nearest neighbors
- data: (NxD) training data; N is the number of samples and D is the
dimensionality of each data point
- labels: training labels
- t_data: (MxD) testing data; M is the number of datapoints and D
is the dimensionality of each data point
- t_labels: testing labels (default = [])
-Output:
- predicted_labels: the predicted labels based on the k-NN
algorithm
- nn_index: the indices of the nearest training data point (Mx1).
- accuracy: if the testing labels are supported, the accuracy of
the classification is returned, otherwise it will be zero.

Cite As

Mahmoud Afifi (2024). kNN classifier (https://www.mathworks.com/matlabcentral/fileexchange/63621-knn-classifier), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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KNN_

Version Published Release Notes
1.1.0.2

..

1.1.0.1

.

1.1.0.0

Bug fixed

1.0.0.0