… nature of skill transfer learning where the Self Organizing Map (SOM) has been used as
the enabling technology. The Growing Self-Organizing Map (GSOM) algorithm is an …

… In the SOM algorithm (Kohonen map), the dimensions of the data are cut through the self-organizing
neural network application. This technique aims to reduce the dimensions of the …

… We apply clustering on the word vectors extracted from all training documents, thus
obtaining a set of k clusters. A document D is then represented as a bag-of-word-embeddings …

… between data features makes the Self Organizing Map (SOM) a … SOM is trained using
DGA results extracted from actual … of AI used in DGA interpretation are ie neural networks [5]–[9]…

… In these methods, an important aspect for a correct modeling is the information extracted
from the sensor measurements. For RGB-D data, these correspond to properties of the shape (…

… modeling of firm history using self-organizing neural networks and a segmentation of the
data … to extract meaningful information from different self-organizing neural networks and that …

… In this paper, the feature is extracted per speaker. The features obtained will be
performed further for dimensionality reduction using SOFM (self-organizing feature map). …

… There is a wide variety of mapping principles in neurobiology studies, but, in this work, we
employ the Kohonen’s SOM (see [14]) in an artificial neural network field. The SOM algorithm …

… data, but it is difficult to extract and analyze the most relevant information. Therefore, …
One artificial neural network, proposed by Teuvo Kohonen in 1982, is the self-organizing …

… Among the various types of neural networks, there are the self-organizing maps (SOMs) or
Kohonen … A standard data extraction form was used to extract and retrieve the data. For the …