It is an unsupervised learning algorithm, and does not require a target vector since it learns to classify data without supervision. A SOM is formed from a grid of nodes or units to which the input data are presented. Every node is connected to the input, and there is no connection between the nodes.
SOM is a topology preserving technique and keeps the neighborhood relations in its mapping presentation. Each node is examined to find the one which its weights are most similar to the input vector. This selection is done by Euclidean distance formula, which is a measure of similarity between two datasets. The distance between the input vector and the weights of node is calculated in order to find the BMU. The Self-Organizing Map operator is applied on the 'Sonar' data set.
The number of dimensions parameter is set to 2. Thus the resultant ExampleSet will be composed of 2 dimensions artificial attributes. You can see the resultant ExampleSet in the Results Workspace and verify that it has only 2 attributes. Please note that these attributes are not original attributes of the 'Sonar' data set.
These attributes were created using the SOM procedure. Documentation 9. The user can specify the required number of dimensions. Description A self-organizing map SOM or self-organizing feature map SOFM is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional typically two-dimensional , discretized representation of the input space of the training samples, called a map. Output example set output IOObject The dimensionality reduction of the given ExampleSet is performed based on a self-organizing map and the resultant ExampleSet is delivered through this port.
In total, there will be net size to the power of number of dimensions nodes in the net. The strength will decrease every round until it reaches the learning rate end in the last round. The strength will decrease to this value in last round, beginning with learning rate start in the first round. This radius decreases every round, starting by adaption radius start in the first round, to adaption radius end in the last round.
By Emil St. Chifu and Ioan Alfred Letia. By Mahira M. Mowjoon and Johnson I Agbinya.
This is made possible by the EU reverse charge method. Edited by Mahmood A. Edited by Ciza Thomas. Edited by Harun Pirim. Edited by Bishnu Pal.
A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to. A self-organizing map (SOM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a.
Edited by Alexander Kokorin. Edited by Theophanides Theophile. Edited by Kresimir Delac.
Edited by Sergey Mikhailov. Edited by Sylvie Manguin. Published: April 1st DOI: Costa Open access peer-reviewed 5.
Araujo Open access peer-reviewed