Logo Recognition by Recursive Neural Networks
GREC '97 Selected Papers from the Second International Workshop on Graphics Recognition, Algorithms and Systems
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
A Recurrent Self-Organizing Map for Temporal Sequence Processing
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
A Bag of Strings Representation for Image Categorization
Journal of Mathematical Imaging and Vision
The graph neural network model
IEEE Transactions on Neural Networks
EnvSOM: a SOM algorithm conditioned on the environment for clustering and visualization
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Clustering XML documents using self-organizing maps for structures
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
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Recent developments with self-organizing maps allow the application to graph structured data. This paper proposes a supervised learning technique for self-organizing maps for structured data. The ideas presented in this paper differ from Kohonen's approach in that a rejection term is introduced. This approach is superior because it is more robust to the variation of the number of different classes in a dataset. It is also more flexible because it is able to efficiently process data with missing or incomplete class information, and hence, includes the unsupervised version as a special case. We demonstrate the capabilities of the proposed model through an application to a relatively large practical data set from the area of image recognition, viz., logo recognition. It is shown that by adding supervised learning to the learning process the discrimination between pattern classes is enhanced, while the computational complexity is similar to that of the unsupervised version.