Extracting salient dimensions for automatic SOM labeling
Learning in self-organizing maps (SOM) is considered unsupervised because training patterns do not need accompanying desired output information. Prior to its use in some real-world applications, however, a trained SOM often has to be labeled. This labeling phase is usually supervised in that labeled...
Saved in:
Main Authors: | Azcarraga, Arnulfo P., Hsieh, Ming Huei, Pan, Shah L., Setiono, Rudy |
---|---|
Format: | text |
Published: |
Animo Repository
2005
|
Subjects: | |
Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/505 https://animorepository.dlsu.edu.ph/context/faculty_research/article/1504/type/native/viewcontent |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | De La Salle University |
Similar Items
-
Extracting salient dimensions for automatic SOM labeling
by: Azcarraga, A.P., et al.
Published: (2013) -
Improved SOM labeling methodology for data mining applications
by: Azcarraga, Arnulfo P., et al.
Published: (2008) -
Improved SOM labeling methodology for data mining applications
by: Azcarraga, A., et al.
Published: (2014) -
Validating the stable clustering of songs in a structured 3D SOM
by: Azcarraga, Arnulfo P., et al.
Published: (2016) -
Design of a structured 3D SOM as a music archive
by: Azcarraga, Arnulfo P., et al.
Published: (2011)