Show simple item record

dc.contributor.authorOyana, Tonny J.en_US
dc.contributor.authorAchenie, Luke E. K.en_US
dc.contributor.authorHeo, Joonen_US
dc.date.accessioned2014-06-12T13:28:56Z
dc.date.available2014-06-12T13:28:56Z
dc.date.issued2012
dc.identifier.citationTonny J. Oyana, Luke E. K. Achenie, and Joon Heo, "The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data," Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 683265, 14 pages, 2012. doi:10.1155/2012/683265
dc.identifier.issn1748-670X
dc.identifier.urihttp://hdl.handle.net/10919/48910
dc.description.abstractThe objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.
dc.description.sponsorshipResponsive and Reflective University Initiative (RRUI), Southern Illinois Unversity
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherHindawi Publishing Corporation
dc.rightsCreative Commons Attribution 3.0 Unported*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/*
dc.subjectSelf-organizing mapsen_US
dc.subjectPID controlen_US
dc.subjectPollutionen_US
dc.subjectDesignen_US
dc.subjectMathematical & computational biologyen_US
dc.titleThe New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Dataen_US
dc.typeArticle - Refereeden_US
dc.rights.holderCopyright © 2012 Tonny J. Oyana et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.identifier.urlhttp://www.hindawi.com/journals/cmmm/2012/683265/cta/
dc.date.accessed2014-06-11
dc.title.serialComputational and Mathematical Methods in Medicine
dc.identifier.doihttps://doi.org/10.1155/2012/683265
dc.type.dcmitypeTexten_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Creative Commons Attribution 3.0 Unported
License: Creative Commons Attribution 3.0 Unported