Self-organizing maps of symbol strings Export

@article{citeulike:2966720, abstract = {Unsupervised self-organizing maps (SOMs), as well as supervised learning by Learning Vector Quantization (LVQ) can be defined for string variables, too. Their computing becomes possible when the SOM and the LVQ algorithms are expressed as batch versions, and when the average over a list of symbol strings is defined to be the string that has the smallest sum of generalized distance functions from all the other strings.}, author = {Kohonen, T.}, citeulike-article-id = {2966720}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0925-2312(98)00031-9}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0925-2312(98)00031-9}, citeulike-linkout-2 = {http://www.sciencedirect.com/science/article/B6V10-3V7S70G-3/1/e7d440f4b77023f8482ce174e8327dbc}, day = {6}, doi = {10.1016/S0925-2312(98)00031-9}, issn = {09252312}, journal = {Neurocomputing}, keywords = {kohonengroup, som, textmining}, month = {November}, number = {1-3}, pages = {19--30}, posted-at = {2008-07-09 03:34:27}, priority = {2}, title = {Self-organizing maps of symbol strings}, url = {http://dx.doi.org/10.1016/S0925-2312(98)00031-9}, volume = {21}, year = {1998} }

TY - JOUR ID - citeulike:2966720 L3 - citeulike-article-id:2966720 N2 - Unsupervised self-organizing maps (SOMs), as well as supervised learning by Learning Vector Quantization (LVQ) can be defined for string variables, too. Their computing becomes possible when the SOM and the LVQ algorithms are expressed as batch versions, and when the average over a list of symbol strings is defined to be the string that has the smallest sum of generalized distance functions from all the other strings. IS - 1-3 JF - Neurocomputing SN - 09252312 EP - 30 TI - Self-organizing maps of symbol strings VL - 21 SP - 19 KW - kohonengroup KW - som KW - textmining AU - Kohonen, T PY - 1998/11/06/ UR - http://dx.doi.org/10.1016/S0925-2312(98)00031-9 ER -