Please use this identifier to cite or link to this item:
|Type:||Artigo de evento|
|Title:||A Supervised Constructive Neuro-immune Network For Pattern Classification|
De Castro L.N.
Von Zuben F.J.
|Abstract:||This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by highperformance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets. © 2006 IEEE.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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