Kohonen neural networks and genetic classification
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| Title: | Kohonen neural networks and genetic classification |
|---|---|
| Authors: | Bianchi, Daniela1 danielabianchi12@virgilio.it, Calogero, Raffaele2 raffaele.calogero@unito.it, Tirozzi, Brunello1 brunello.tirozzi@roma1.infn.it |
| Source: | Mathematical & Computer Modelling. Jan2007, Vol. 45 Issue 1/2, p34-60. 27p. |
| Subjects: | Artificial neural networks, Artificial intelligence, Algorithms, Stochastic processes |
| Abstract: | Abstract: We discuss the property of a.e. and in mean convergence of the Kohonen algorithm considered as a stochastic process. The various conditions ensuring a.e. convergence are described and the connection with the rate decay of the learning parameter is analyzed. The rate of convergence is discussed for different choices of learning parameters. We prove rigorously that the rate of decay of the learning parameter which is most used in the applications is a sufficient condition for a.e. convergence and we check it numerically. The aim of the paper is also to clarify the state of the art on the convergence property of the algorithm in view of the growing number of applications of the Kohonen neural networks. We apply our theorem and considerations to the case of genetic classification which is a rapidly developing field. [Copyright &y& Elsevier] |
| Copyright of Mathematical & Computer Modelling is the property of Pergamon Press - An Imprint of Elsevier Science 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: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 22582260 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Kohonen neural networks and genetic classification – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bianchi%2C+Daniela%22">Bianchi, Daniela</searchLink><relatesTo>1</relatesTo><i> danielabianchi12@virgilio.it</i><br /><searchLink fieldCode="AR" term="%22Calogero%2C+Raffaele%22">Calogero, Raffaele</searchLink><relatesTo>2</relatesTo><i> raffaele.calogero@unito.it</i><br /><searchLink fieldCode="AR" term="%22Tirozzi%2C+Brunello%22">Tirozzi, Brunello</searchLink><relatesTo>1</relatesTo><i> brunello.tirozzi@roma1.infn.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mathematical+%26+Computer+Modelling%22">Mathematical & Computer Modelling</searchLink>. Jan2007, Vol. 45 Issue 1/2, p34-60. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: We discuss the property of a.e. and in mean convergence of the Kohonen algorithm considered as a stochastic process. The various conditions ensuring a.e. convergence are described and the connection with the rate decay of the learning parameter is analyzed. The rate of convergence is discussed for different choices of learning parameters. We prove rigorously that the rate of decay of the learning parameter which is most used in the applications is a sufficient condition for a.e. convergence and we check it numerically. The aim of the paper is also to clarify the state of the art on the convergence property of the algorithm in view of the growing number of applications of the Kohonen neural networks. We apply our theorem and considerations to the case of genetic classification which is a rapidly developing field. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mathematical & Computer Modelling is the property of Pergamon Press - An Imprint of Elsevier Science 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.mcm.2006.04.004 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 34 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Stochastic processes Type: general Titles: – TitleFull: Kohonen neural networks and genetic classification Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bianchi, Daniela – PersonEntity: Name: NameFull: Calogero, Raffaele – PersonEntity: Name: NameFull: Tirozzi, Brunello IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 08957177 Numbering: – Type: volume Value: 45 – Type: issue Value: 1/2 Titles: – TitleFull: Mathematical & Computer Modelling Type: main |
| ResultId | 1 |
