@article{oai:kyutech.repo.nii.ac.jp:00001543,
 author = {Muslim, M. Aziz and Ishikawa, Masumi and 石川, 眞澄 and Furukawa, Tetsuo and 古川, 徹生},
 issue = {6},
 journal = {Neural Computing and Applications},
 month = {Oct},
 note = {Proposed is a new approach to task segmentation in a mobile robot by a modular network SOM (mnSOM). In a mobile robot, however, the standard mnSOM is not applicable as it is, because it is based on the assumption that class labels are known a priori. In a mobile robot, only a sequence of data without segmentation is available. Hence, we propose to decompose it into many subsequences, supposing that a class label does not change within a subsequence. Accordingly, training of mnSOM is done for each subsequence in contrast to that for each class in the standard mnSOM. The resulting mnSOM demonstrates good segmentation performance of 94.05% for a novel dataset.},
 pages = {571--580},
 title = {Task segmentation in a mobile robot by mnSOM: a new approach to training expert modules},
 volume = {16},
 year = {2007},
 yomi = {イシカワ, マスミ and フルカワ, テツオ}
}