setLearningPar: Sets learning parameters for SOM
In hosscine/SME: Self-Manifolding Embedding
Description
Sets learning parameters for SOM
Usage
1
setLearningPar(som, alpha = 0.1, sigma = 1, neighbor = 2)
Arguments
som
target som.
alpha
learning rate.
sigma
variance of the weights propagation.
neighbor
size of neighborhood kernel about the nodes. 1 indicates the neighbors of a node are its one adjacents.
hosscine/SME documentation built on May 14, 2019, 12:45 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description
Sets learning parameters for SOM
Usage
1 | setLearningPar(som, alpha = 0.1, sigma = 1, neighbor = 2)
|
Arguments
som |
target som. |
alpha |
learning rate. |
sigma |
variance of the weights propagation. |
neighbor |
size of neighborhood kernel about the nodes. 1 indicates the neighbors of a node are its one adjacents. |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.