Difference between revisions of "Self-Organizing"
| Line 16: | Line 16: | ||
* [http://blog.yhat.com/posts/self-organizing-maps-2.html Self-Organising Maps: In Depth | David Asboth - The Yhat Blog] | * [http://blog.yhat.com/posts/self-organizing-maps-2.html Self-Organising Maps: In Depth | David Asboth - The Yhat Blog] | ||
* [[Case Studies]] | * [[Case Studies]] | ||
| + | |||
| + | Self-Organizing Networks (SON) significantly improve performance by automatically identifying and fixing problems. | ||
| + | |||
| + | Self Organizing Map(SOM) by Teuvo Kohonen provides a data visualization technique which helps to understand high dimensional data by reducing the dimensions of data to a map. SOM also represents clustering concept by grouping similar data together. Therefore it can be said that SOM reduces data dimensions and displays similarities among data. With SOM, clustering is performed by having several units compete for the current object. Once the data have been entered into the system, the network of artificial neurons is trained by providing information about inputs. The weight vector of the unit is closest to the current object becomes the winning or active unit. During the training stage, the values for the input variables are gradually adjusted in an attempt to preserve neighborhood relationships that exist within the input data set. As it gets closer to the input object, the weights of the winning unit are adjusted as well as its neighbors. [http://www.pitt.edu/~is2470pb/Spring05/FinalProjects/Group1a/tutorial/som.html Self-Organizing Maps | Jae-Wook Ahn and Sue Yeon Syn] | ||
| + | |||
| + | http://m.media-amazon.com/images/I/71b16QrscQL._AC_UY218_ML3_.jpg | ||
| + | |||
| + | http://www.pitt.edu/~is2470pb/Spring05/FinalProjects/Group1a/tutorial/kohonen1.gif | ||
<youtube>H9H6s-x-0YE</youtube> | <youtube>H9H6s-x-0YE</youtube> | ||
Revision as of 18:52, 8 September 2019
YouTube search... ...Google search
- Bio-inspired Computing
- (Artificial) Immune System
- Cybersecurity
- Self Organizing Maps | Abhinav Ralhan - Towards Data Science
- Self-Organizing Network (SON) | Wikipedia
- Self-organizing Map | Wikipedia
- Self-Organising Maps: In Depth | David Asboth - The Yhat Blog
- Case Studies
Self-Organizing Networks (SON) significantly improve performance by automatically identifying and fixing problems.
Self Organizing Map(SOM) by Teuvo Kohonen provides a data visualization technique which helps to understand high dimensional data by reducing the dimensions of data to a map. SOM also represents clustering concept by grouping similar data together. Therefore it can be said that SOM reduces data dimensions and displays similarities among data. With SOM, clustering is performed by having several units compete for the current object. Once the data have been entered into the system, the network of artificial neurons is trained by providing information about inputs. The weight vector of the unit is closest to the current object becomes the winning or active unit. During the training stage, the values for the input variables are gradually adjusted in an attempt to preserve neighborhood relationships that exist within the input data set. As it gets closer to the input object, the weights of the winning unit are adjusted as well as its neighbors. Self-Organizing Maps | Jae-Wook Ahn and Sue Yeon Syn
