RABNET: a real-valued antibody network for data clustering

This paper proposes a novel constructive learning algorithm for a competitive neural network. The proposed algorithm is developed by taking ideas from the immune system and demonstrates robustness in the initial experiments reported here for a benchmark problem. Comparisons with results from the literature are also provided. To automatically segment the resultant neurons at the output, a tool from graph theory was used with promising results. General discussions and avenues for future works are also provided. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning - connectionism and neural nets, I.5.3 [Pattern Recognition]: Clustering – Algorithms; General Terms Algorithms, Design. Keywords Artificial Immune Systems, Data Clustering, Artificial Neural Networks

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Helder Knidel, Leandro Nunes de Castro, Fernando J. Von Zuben
• Keywords: Competitive Neural Network, Constructive Learning Algorithm, GECCO 2005, Neural Networks