Magnification Control in Self-Organizing Maps and Neural Gas.
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| Title: | Magnification Control in Self-Organizing Maps and Neural Gas. |
|---|---|
| Authors: | Villmann, Thomas, Claussen, Jens Christian |
| Source: | Neural Computation. Feb2006, Vol. 18 Issue 2, p446-469. 24p. |
| Subjects: | Self-organizing maps, Artificial neural networks, Self-organizing systems, Learning, Algorithms |
| Abstract: | We consider different ways to control the magnification in self-organizing maps (SOM) and neural gas (NG). Starting from early approaches of magnification control in vector quantization, we then concentrate on different approaches for SOM and NG. We show that three structurally similar approaches can be applied to both algorithms that are localized learning, concave-convex learning, and winner-relaxing learning. Thereby, the approach of concave-convex learning in SOM is extended to a more general description, whereas the concave-convex learning for NG is new. In general, the control mechanisms generate only slightly different behavior comparing both neural algorithms. However, we emphasize that the NG results are valid for any data dimension, whereas in the SOM case, the results hold only for the one-dimensional case. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 19099460 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Magnification Control in Self-Organizing Maps and Neural Gas. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Villmann%2C+Thomas%22">Villmann, Thomas</searchLink><br /><searchLink fieldCode="AR" term="%22Claussen%2C+Jens+Christian%22">Claussen, Jens Christian</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Feb2006, Vol. 18 Issue 2, p446-469. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Self-organizing+maps%22">Self-organizing maps</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Self-organizing+systems%22">Self-organizing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We consider different ways to control the magnification in self-organizing maps (SOM) and neural gas (NG). Starting from early approaches of magnification control in vector quantization, we then concentrate on different approaches for SOM and NG. We show that three structurally similar approaches can be applied to both algorithms that are localized learning, concave-convex learning, and winner-relaxing learning. Thereby, the approach of concave-convex learning in SOM is extended to a more general description, whereas the concave-convex learning for NG is new. In general, the control mechanisms generate only slightly different behavior comparing both neural algorithms. However, we emphasize that the NG results are valid for any data dimension, whereas in the SOM case, the results hold only for the one-dimensional case. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=19099460 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/089976606775093918 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 446 Subjects: – SubjectFull: Self-organizing maps Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Self-organizing systems Type: general – SubjectFull: Learning Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Magnification Control in Self-Organizing Maps and Neural Gas. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Villmann, Thomas – PersonEntity: Name: NameFull: Claussen, Jens Christian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2006 Type: published Y: 2006 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 18 – Type: issue Value: 2 Titles: – TitleFull: Neural Computation Type: main |
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