Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:convolutional_neural_network
unsupervised learning algorithm |
| gptkbp:abbreviation |
SOM
|
| gptkbp:activatedBy |
winner-takes-all
|
| gptkbp:alternativeName |
gptkb:Kohonen_map
gptkb:Kohonen_network |
| gptkbp:application |
speech recognition
bioinformatics pattern recognition feature extraction data mining image analysis market segmentation |
| gptkbp:category |
gptkb:artificial_intelligence
gptkb:machine_learning data analysis neural computation |
| https://www.w3.org/2000/01/rdf-schema#label |
Self-Organizing Map
|
| gptkbp:influencedBy |
biological neural networks
|
| gptkbp:input |
high-dimensional data
|
| gptkbp:introducedIn |
1982
|
| gptkbp:inventedBy |
gptkb:Teuvo_Kohonen
|
| gptkbp:learningRule |
competitive learning
|
| gptkbp:networkStructure |
grid of neurons
|
| gptkbp:neuronArrangement |
rectangular grid
hexagonal grid |
| gptkbp:notablePublication |
Self-Organizing Maps (book by Teuvo Kohonen)
|
| gptkbp:openSource |
gptkb:MiniSom
gptkb:SOMPY Kohonen package (R) SOM Toolbox for Matlab SOM-Learn |
| gptkbp:output |
low-dimensional (typically 2D) map
|
| gptkbp:parameter |
learning rate
number of iterations map size neighborhood radius |
| gptkbp:preserves |
topological properties of input space
|
| gptkbp:relatedTo |
gptkb:principal_component_analysis
k-means clustering vector quantization |
| gptkbp:trainer |
unsupervised
|
| gptkbp:updateRule |
neighborhood function
weight adaptation |
| gptkbp:usedFor |
gptkb:data_visualization
clustering dimensionality reduction |
| gptkbp:visualizes |
topological map
|
| gptkbp:bfsParent |
gptkb:convolutional_neural_network
|
| gptkbp:bfsLayer |
5
|