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Task segmentation in a mobile robot by mnSOM and clustering with spatio-temporal contiguity

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Abstract

In our previous study, task segmentation by mnSOM implicitly assumes that winner modules corresponding to subsequences in the same class share the same label. This paper proposes to do task segmentation by applying various clustering methods to the resulting mnSOM without using the above assumption. Firstly we use the conventional hierarchical clustering. It assumes that the distances between any pair of modules are provided with precision, but this is not exactly true. Accordingly, this is followed by a clustering based on only the distance between spatially adjacent modules with modification by their temporal contiguity. This clustering with spatio-temporal contiguity provides superior performance to the conventional hierarchical clustering and comparable performance with mnSOM using the implicit assumption.

Original languageEnglish
Title of host publicationNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Pages1075-1084
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
Duration: 13 Nov 200716 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4985 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Neural Information Processing, ICONIP 2007
Country/TerritoryJapan
CityKitakyushu
Period13/11/0716/11/07

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