The self-organizing map Export

@inproceedings{kohonen:som, abstract = {The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed}, author = {Kohonen, T.}, booktitle = {Proceedings of the IEEE}, citeulike-article-id = {463065}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/5.58325}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=58325}, day = {06}, doi = {10.1109/5.58325}, journal = {Proceedings of the IEEE}, keywords = {chi2007, colourplayer, imagingvis}, month = {August}, number = {9}, pages = {1464--1480}, posted-at = {2006-04-04 14:16:51}, priority = {2}, title = {The self-organizing map}, url = {http://dx.doi.org/10.1109/5.58325}, volume = {78}, year = {2002} }

TY - CONF ID - kohonen:som L3 - citeulike-article-id:463065 N2 - The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed IS - 9 JF - Proceedings of the IEEE EP - 1480 TI - The self-organizing map VL - 78 T2 - Proceedings of the IEEE SP - 1464 KW - chi2007 KW - colourplayer KW - imagingvis AU - Kohonen, T PY - 2002/08/06/ UR - http://dx.doi.org/10.1109/5.58325 ER -