Title :
A Supervised Constructive Neuro-Immune Network for Pattern Classification
Author :
Knidel, Helder ; De Castro, Leandro Nunes ; Von Zuben, Fernando J.
Author_Institution :
Campinas Univ., Campinas
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 high-performance, 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.
Keywords :
learning (artificial intelligence); pattern classification; vector quantisation; classification algorithm; computational cost; constructive neuro-immune network; convergence criterion; immune system; learning algorithm; learning vector quantization; pattern classification; supervised constructive neuro-immune network; Artificial neural networks; Classification algorithms; Clustering algorithms; Immune system; Neurons; Pattern classification; Prototypes; Supervised learning; Unsupervised learning; Vector quantization;
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
DOI :
10.1109/IJCNN.2006.246978