Hagenbuchner, M., Sperduti, A., and Tsoi, A.C. (2003). A Self-Organizing Map for Adaptive Processing of Structured Data. IEEE Transactions on Neural Networks 14:191-505. Home/Search Document Not in Database Summary Related Articles Check |
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Neural Gas for Sequences - Strickert, Hammer (2003) (Correct)
....has been proposed, a class of which using embeddings into a finite dimensional vector space [9, 12] but for which a standard rectangular lattice or the Euclidean metric is seldom appropriate for matching the possibly complex data topology. Other approaches model sequential data recursively [3, 4, 13, 14, 15], see e.g. 1] for an overview. Unsupervised sequence processors using recurrent connections recursively compare single sequence elements. Automatically, the similarity structure of entire sequences emerges by the integration of the recursive comparison. In contrast to their supervised recurrent ....
....recursive comparison. In contrast to their supervised recurrent networks counterparts, a large variety of unsupervised recurrent selforganizing models exists: the temporal Kohonen map (TKM) the recurrent SOM (RSOM) recursive SOM (RecSOM) and SOM for structured data (SOMSD) to name just a few [3, 4, 14, 15]. It is not clear which model performs best in certain situations, and the principle capacities of those approaches, their similarities and differences are only partially understood. Recently, a general framework has been proposed to cover these models in a unifying notation [5, 6] the models ....
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M. Hagenbuchner, A. Sperduti, and A. Tsoi. A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks, 14:491--505, 2003.
Self-Organizing Maps for Time Series - Barbara Hammer Alessio Self-citation (Sperduti) (Correct)
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Hagenbuchner, M., Sperduti, A., and Tsoi, A.C. (2003). A Self-Organizing Map for Adaptive Processing of Structured Data. IEEE Transactions on Neural Networks 14:191-505.
A Self-Organising Map Approach for Clustering of XML - Documents Trentini And Self-citation (Hagenbuchner Sperduti Tsoi) (Correct)
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M. Hagenbuchner, A. Sperduti, and A. Tsoi. A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks, 14(3):491--505, May 2003.
Contextual Processing of Graphs using - Self-Organizing Maps Markus Self-citation (Hagenbuchner Sperduti Tsoi) (Correct)
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M. Hagenbuchner, A. Sperduti, and A.C. Tsoi. A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks, 14(3):491--505, May 2003.
Unsupervised Clustering of Continuous - Trajectories Of Kinematic Self-citation (Sperduti) (Correct)
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M. Hagenbuchner, A. Sperduti, and A.C. Tsoi. A self-organizing map for adaptive processing of structured data. IEEE Trans. on Neural Networks, 14(3):491--505, May 2003.
A General Framework for Unsupervised Processing of.. - Hammer, Micheli, al. (2002) (1 citation) Self-citation (Sperduti) (Correct)
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M. Hagenbuchner, A. Sperduti, and A.C. Tsoi. A Self-Organizing Map for Adaptive Processing of Structured Data. IEEE Transactions on Neural Networks, 14(3):491-- 505, 2003.
A General Framework for Self-Organizing Structure.. - Hammer, Micheli.. Self-citation (Sperduti) (Correct)
....dynamics such as leaky integrators or more general recurrent connections which allow the recursive processing of sequences. Examples are the temporal Kohonen map (TKM) 7] the recursive SOM (RecSOM) 45, 46, 47] or the approaches proposed in [9, 24, 25, 31] The SOM for structured data (SOMSD) [17, 18, 41] constitutes a recursive mechanism capable of processing tree structured data and thus also sequences in an unsupervised way. Alternative models for unsupervised time series processing use for example hierarchical network architectures. An overview of important models can be found e.g. in [3] We ....
....I(t i ) denotes the winning index of the subtree t i of the currently processed part of the tree. The neighborhood is updated into the same direction with corresponding smaller learning rate. This method has been used for the classification of pictures represented through tree structured data [17, 18]. During learning, a representation of the pictures in the SOMSD emerges which groups pictures described by similarly structured trees with similar labels together. In the reported experiments, the synthetically designed pictures correspond to ships, houses, and policemen with various shapes and ....
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M. Hagenbuchner, A. Sperduti, and A.C. Tsoi. A Self-Organizing Map for Adaptive Processing of Structured Data. to appear in IEEE Transactions on Neural Networks.
Comparing the PLSOM and the SOM over normal - Spaces (Correct)
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M. Hagenbuchner, A. Sperduti, and A. C. Tsoi. A self-organizing map for adaptive processing of structured data. Neural Networks, IEEE Transactions on, 14(3):491 -- 505, 5 2003.
Self-Organizing Neural Networks for Sequence Processing - Strickert (Correct)
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M. Hagenbuchner, A. Sperduti, and A. Tsoi. A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks, 14(3):491--505, 2003.
Neural Methods for Non-Standard Data - Hammer, Jain (Correct)
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M. Hagenbuchner, A. Sperduit, and A.C. Tsoi. A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks 14(3):491-505, 2003.
Mathematical Aspects of Neural Networks - Hammer (2003) (Correct)
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M. Hagenbuchner, A. Sperduti, and A.C. Tsoi. A Self-Organizing Map for Adaptive Processing of Structured Data. to appear in IEEE Transactions on Neural Networks.
Neural Gas for Sequences - Marc Strickert And (2003) (Correct)
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M. Hagenbuchner, A. Sperduti, and A. Tsoi. A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks, 14:491--505, 2003.
Self-Organizing Context Learning - Strickert, Hammer (2004) (Correct)
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M. Hagenbuchner, A. Sperduti, and A. Tsoi. A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks, 14(3):491--505, 2003.
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