This approach enables the discovery of similarities among objects including vectors consisting of numerical data. The capabilities of the model are analyzed in ...
In this paper, we will show how self-organizing maps (SOMs) [22] can be extended to treat complex objects rep- resented by data structures. A SOM implements a ...
Recent developments in the area of neural networks produced models capable of dealing with structured data. Here, we propose the first fully unsupervised ...
This work proposes the first fully unsupervised model, namely an extension of traditional self-organizing maps (SOMs), for the processing of labeled ...
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This paper introduces a new machine learning paradigm specifically intended for classification problems where the classes have a natural order. The technique ...
TL;DR: This work proposes the first fully unsupervised model, namely an extension of traditional self-organizing maps (SOMs), for the processing of labeled ...
Hagenbuchner, M., Sperduti, A. and Tsoi, A.C. (2003) A Self-Organizing Map for Adaptive Processing of Structured Data. IEEE Transactions on Neural Networks, 14, ...
Hagenbuchner, M., Sperduti, A., & Tsoi, A. C. (2003). A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks, 14 ...
A self organizing map (SOM) for processing of structured data, using an unsupervised learning approach, called SOM-SD, has recently been proposed.
The structure-adaptive SOM places the nodes of prototype vectors into the pattern space properly so as to make the decision boundaries as close to the class ...