Bibliographic Details
| 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] |
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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 |
