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Strategies for unsupervised multimedia processing: self-organizing trees and forests

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4 Author(s)
Kyan, M. ; Ryerson Univ., Toronto, Ont. ; Jarrah, K. ; Muneesawang, P. ; Ling Guan

In this article, we explore a new family of neural network architectures that have a basis in self-organization, yet are somewhat free from many of the constraints typical of other well-known self-organizing architectures. Within this family, the basic processing unit is known as the self-organizing tree map (SOTM). We will look at how this model has evolved since its inception in 1995, how it has inspired new models, and how it is being applied to complex multimedia research problems in digital asset management and microbiological image analysis

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Computational Intelligence Magazine, IEEE  (Volume:1 ,  Issue: 2 )