In this paper, a class-modeling technique based on Kohonen artificial neural networks is presented. In particular, in order for the Kohonen self-organizing map to operate as a class-modeling device, two main issues are identified: integrating the training set (composed of samples from a single category) with a set of uniformly distributed random vectors and computing a suitable probability distribution associated to the positions on the 2D layer of neurons. Both the identified features concur in defining an opportune class space. When used to analyze a real-world data set (classification of rice varieties), the proposed technique provided comparable and in some cases better results than the traditional chemometric techniques SIMCA and UNEQ. (c) 2004 Elsevier B.V. All rights reserved.
Class-modeling using Kohonen artificial neural networks / Marini, Federico; Jure, Zupan; Magri', Antonio. - In: ANALYTICA CHIMICA ACTA. - ISSN 0003-2670. - STAMPA. - 544:1-2 SPEC. ISS.(2005), pp. 306-314. (Intervento presentato al convegno 9th International Conference on Chemometrics in Analytical Chemistry tenutosi a Lisbon, PORTUGAL nel SEP 20-23, 2004) [10.1016/j.aca.2004.12.026].
Class-modeling using Kohonen artificial neural networks
MARINI, Federico;MAGRI', Antonio
2005
Abstract
In this paper, a class-modeling technique based on Kohonen artificial neural networks is presented. In particular, in order for the Kohonen self-organizing map to operate as a class-modeling device, two main issues are identified: integrating the training set (composed of samples from a single category) with a set of uniformly distributed random vectors and computing a suitable probability distribution associated to the positions on the 2D layer of neurons. Both the identified features concur in defining an opportune class space. When used to analyze a real-world data set (classification of rice varieties), the proposed technique provided comparable and in some cases better results than the traditional chemometric techniques SIMCA and UNEQ. (c) 2004 Elsevier B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.