Unsupervised Segmentation With Dynamical Units

In this paper, we present a novel network to separate mixtures of inputs that have been previously learned. A significant capability of the network is that it segments the components of each input object that most contribute to its classification. The network consists of amplitude-phase units that c...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2008-01, Vol.19 (1), p.168-182
Hauptverfasser:	Ravishankar Rao, A., Cecchi, G.A., Peck, C.C., Kozloski, J.R.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Architecture Artificial Intelligence Binding problem Biological information theory Biology computing Classification Computer architecture Computer science control theory systems Computer systems and distributed systems. User interface Connectionism. Neural networks Deconvolution Dynamics Exact sciences and technology Humans Layout Learning - physiology Nerve Net Networks Neural networks Neural Networks (Computer) Nonlinear Dynamics oscillations Pattern Recognition, Automated phase correlation Quantum computing Segmentation Segments separation of mixtures Similarity Software Studies synchronization Visual system
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