m
Line 28: Line 28:
 
== References==
 
== References==
  
"14.2-Clustering-KMeansAlgorithm- Machine Learning - Professor Andrew Ng"
+
"14.2-Clustering-KMeansAlgorithm- Machine Learning - Professor Andrew Ng",<br />
https://www.youtube.com/watch?v=Ao2vnhelKhI
+
https://www.youtube.com/watch?v=Ao2vnhelKhI.
  
Stanford CS 221,"K Means"
+
Stanford CS 221,"K Means", <br />
 
http://stanford.edu/~cpiech/cs221/handouts/kmeans.html.
 
http://stanford.edu/~cpiech/cs221/handouts/kmeans.html.
  
Purdue ECE 438, "ECE438 - Laboratory 9: Speech Processing (Week 1)", October 6, 2010,  
+
Purdue ECE 438, "ECE438 - Laboratory 9: Speech Processing (Week 1)", October 6, 2010,<br />
 
https://engineering.purdue.edu/VISE/ee438L/lab9/pdf/lab9a.pdf.
 
https://engineering.purdue.edu/VISE/ee438L/lab9/pdf/lab9a.pdf.
 
 
  
 
----
 
----
 
 
  
  
 
[[ 2017 Spring ECE 438 Boutin|Back to 2017 Spring ECE 438 Boutin]]
 
[[ 2017 Spring ECE 438 Boutin|Back to 2017 Spring ECE 438 Boutin]]

Revision as of 14:47, 19 April 2017


Quantization and Classification using K-Means Clustering

by Sara Wendte


Introduction

K-means clustering is a simple unsupervised learning method. This method can be applied to implement color quantization in an image by finding clusters of pixel values. Another useful application would be automatic classification of phonemes in a speech signal by finding clusters of formant values for different speakers.


Background


Theory


Color Quantization Application


Phoneme Classification Application


References

"14.2-Clustering-KMeansAlgorithm- Machine Learning - Professor Andrew Ng",
https://www.youtube.com/watch?v=Ao2vnhelKhI.

Stanford CS 221,"K Means",
http://stanford.edu/~cpiech/cs221/handouts/kmeans.html.

Purdue ECE 438, "ECE438 - Laboratory 9: Speech Processing (Week 1)", October 6, 2010,
https://engineering.purdue.edu/VISE/ee438L/lab9/pdf/lab9a.pdf.



Back to 2017 Spring ECE 438 Boutin

Alumni Liaison

has a message for current ECE438 students.

Sean Hu, ECE PhD 2009