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This paper presents a DSOM architecture that is easily parallelizable and hence more computationally efficient (PD-SOM). The presented architecture has ...
This paper presents a DSOM architecture that is easily parallelizable and hence more computationally efficient (PD-SOM). The presented architecture has three ...
This paper presents a DSOM architecture that is easily parallelizable and hence more computationally efficient (PD-SOM). The presented architecture has ...
This paper presents a DSOM architecture that is easily parallelizable and hence more computationally efficient (PD-SOM), and consistently outperformed the ...
DSOM is computationally expensive, and like traditional SOMs, it has the problem of static map size [32] . Reference [32] presents an easily parallelizable Deep ...
TL;DR: This paper presents a DSOM architecture that is easily parallelizable and hence more computationally efficient (PD-SOM), and consistently ...
In this paper, we present a fully parallel SOM hardware architecture, optimized for high-throughput, by reducing the SOM data processing cycle.
Missing: deep classification.
E-DSOM enhances the DSOM in two ways: 1) the learning algorithm is completely unsu- pervised and 2) the architecture learns features of different resolutions in ...
This article describes a workflow for using Self-Organizing Maps (SOM) as a time- and cost-efficient method of visualizing and validating property relationships ...
Oct 22, 2024 · We describe a scalable parallel implementation of the self organizing map (SOM) suitable for data-mining applications involving clustering ...