Application of Growing Hierarchical Self-Organizing Map in Handwritten Digit Recognition

This paper discusses the application of a GH-SOM architecture to the problem of Handwritten Digit Recognition. The results proved to be better than the ones obtained from standard SOM networks.

[1]  Risto Miikkulainen,et al.  Laterally Interconnected Self-Organizing Maps in Hand-Written Digit Recognition , 1995, NIPS.

[2]  Hong Yan,et al.  Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM) , 1999, IEEE Trans. Neural Networks.

[3]  Andreas Rauber,et al.  The growing hierarchical self-organizing map , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[4]  Risto Mukkulainen,et al.  Script Recognition with Hierarchical Feature Maps , 1990 .

[5]  T. Shimada,et al.  A new self-organizing method and its application to handwritten digit recognition , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[6]  Hong Yan,et al.  Combined SOM and LVQ based classifiers for handwritten digit recognition , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[7]  Pasi Koikkalainen,et al.  Self-organizing hierarchical feature maps , 1990, 1990 IJCNN International Joint Conference on Neural Networks.