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Unsupervised Learning of Image Recognition  (Make Corrections)  
with Neural Society for Clustering Marcin Wojnarski Warsaw University,...



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Abstract: New algorithm for partitional data clustering is presented, Neural Society for Clustering (NSC). Its creation was inspired by hierarchical image understanding, which requires unsupervised training to build the hierarchy of visual features. Existing clustering algorithms are not well-suited for this task, since they usually split natural groups of patterns into several parts (like k-means) or give crisp clustering. (Update)

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BibTeX entry:   (Update)

@misc{ society-unsupervised,
  author = "With Neural Society",
  title = "Unsupervised Learning of Image Recognition",
  url = "citeseer.ist.psu.edu/762875.html" }
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2319   Elements of Information Theory (context) - Cover, Thomas - 1991
490   Pattern recognition and neural networks - Ripley - 1996
190   Data clustering: a review - Jain, Murty et al. - 1999
75   Gradient-based learning applied to document recognition - LeCun, Bottou et al. - 1998
35   Hierarchical models of object recognition in cortex - Riesenhuber, Poggio - 1999
4   Hierarchical Neural Networks for Image Interpretation (context) - Behnke - 2003
4   training algorithm and applications of new neural classifier (context) - Wojnarski - 2003
1   Object recognition with features inspired by visual cortex (context) - Serre, Wolf et al. - 2005
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