Self-organizing maps in sequence processing

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Varsta, Markus
dc.date.accessioned 2012-02-10T09:48:40Z
dc.date.available 2012-02-10T09:48:40Z
dc.date.issued 2002-12-12
dc.identifier.isbn 951-22-6252-5
dc.identifier.issn 1455-0474
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2259
dc.description.abstract Models are abstractions of observed real world phenomena or processes. A good model captures the essential properties of the modeled phenomena. In the statistical learning paradigm the processes that generate observations are assumed unknown and too complex for analytical modeling, thus the models are trained from more general templates with measured observations. A substantial part of the processes we seek to model have temporal dependencies between observations thus defining templates that can account for these dependencies improves their ability to capture the properties of such processes. In this work we discuss using the self organizing map with sequentially dependent data. Self-Organizing map (SOM) is perhaps the most popular non supervised neural network model that has found varied applications in the field of data mining for example. The original SOM paradigm, however, considers independent data, where context of a sample does not influence its interpretation. However, throwing away the temporal context of an observation when we know we are dealing with sequential data seems wasteful. Consequently methods for incorporating time into the SOM paradigm have been rather extensively studied. Such models if powerful enough would be very usable when tracking dynamic processes. In this work a Self-Organizing map for temporal sequence processing dubbed Recurrent Self-Organizing Map (RSOM) was proposed and analyzed. The model has been used in time series prediction combined with local linear models. Deeper analysis provides insight into how much and what kind of contextual information the model is able to capture. The other topic covered by the publications in a sense considers an inverse problem. In this topic SOM was used to create sequential dependence and order into initially unordered data by modeling a surface and creating a path over the surface for a surface manipulating robot. en
dc.format.extent 71, [59]
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Helsinki University of Technology Laboratory of Computational Engineering publications. Report B en
dc.relation.ispartofseries 32 en
dc.relation.haspart Varsta, M., and Koikkalainen, P. (1996) Surface Modeling and Robot Path Generation Using Self-Organization. In Proceedings of ICPR '96, pages 30-34. IEEE.
dc.relation.haspart Koikkalainen, P., and Varsta, M. (1996) Robot Path generation for surface processing applications via neural networks. In Proceedings of the SPIE, Vol. 2904, pages 66-73. SPIE.
dc.relation.haspart Varsta, M., Heikkonen, J., and Millán, J. del R. (1997) Epileptic Activity Detection in EEG with Neural Networks. In Proceedings of the 1997 International Conference on Engineering Applications of Neural Networks, pages 179-186. The Royal Institute of Technology, Stockholm.
dc.relation.haspart Varsta, M., Millán, J. del R., and Heikkonen, J., (1997) A Recurrent Self Organizing Map for Temporal Sequence Processing. In ICANN'97: International Conference on Artificial Neural Networks, LNCS vol. 1327, pages 421-426. Springer.
dc.relation.haspart Koskela, T., Varsta, M., Heikkonen, J., and Kaski, K. (1998) Temporal Sequence Processing using Recurrent SOM. In KES '98: Proceedings of the Second International Conference on Knowledge Based Engineering Systems, vol. 1, pages 290-296. IEEE.
dc.relation.haspart Varsta, M., Heikkonen, J., Lampinen, J., and Millán, J. del R. (2001) Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison. Neural Processing Letters, Vol. 13, pages 237-251, Kluwer Academic Publishers.
dc.subject.other Computer science en
dc.title Self-organizing maps in sequence processing en
dc.type G5 Artikkeliväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Electrical and Communications Engineering en
dc.contributor.department Sähkö- ja tietoliikennetekniikan osasto fi
dc.subject.keyword self-organizing maps en
dc.subject.keyword temporal sequence processing en
dc.identifier.urn urn:nbn:fi:tkk-002299
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.contributor.lab Laboratory of Computational Engineering en
dc.contributor.lab Laskennallisen tekniikan laboratorio fi


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