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ijcnn04_raitio_et_al_emerrepr.pdf
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🔗 Source: research.ics.tkk.fi
📊 Size: 168 KB
📄 Pages: 6 pages
⬇️ Downloads: 65

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Researchers assess the similarity of emergent representations based on unsupervised learning, using Self-Organizing Maps (SOM) trained with color spectrum input and various vectorial encodings. The SOM is used for heteroassociative mapping to associate color spectrum with color names, and the recall of association between spectra and colors is assessed. The results show that the SOM learns representations for both stimuli and color names and is able to associate them successfully. The organization is compared through correlation of activation patterns, and experiments demonstrate that the emerged representations for stimuli are similar with respect to the partitioning-of-activation-space measure, almost independently of the encoding used for input representation.

Description

Researchers assess the similarity of emergent representations based on unsupervised learning, using Self-Organizing Maps (SOM) trained with color spectrum...

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  • File Format: PDF
  • File Size: 168 KB
  • Pages: 6
  • Language: EN
  • Total Downloads: 65
  • Last Updated: 3 months ago

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