Generating Hypotheses Using the Multilevel Hypermap Architecture
The Multilevel Hypermap Architecture (MHA) is an extension of the Hypermap introduced by Kohonen. By means of the MHA it is possible to analyze structured or hierarchical data (data with priorities, data with context, time series, data with varying exactness), which is difficult or impossible to do with known self-organizing maps so far.A new adaptation of the learning algorithm and its implications for data analysis is the main aspect of this paper. With the generation of hypotheses the MHA is able to detect untrained data relationships in data sets. Beside the advantages in data analysis this approach can also be a contribution to the field of artificial intelligence.
Bernd Bruckner Henning Hofmeister
Leibniz Institute for Neurobiology P.O.Box 1860, 39008 Magdeburg, Germany
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
96-100
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）