Title :
Agglomerative learning for general fuzzy min-max neural network
Author_Institution :
Appl. Comput. Intelligence Res. Unit, Univ. of Paisley, UK
Abstract :
Proposes an agglomerative learning algorithm based on similarity measures defined for hyperbox fuzzy sets. It is presented in a context of clustering and classification problems that are tackled using a general fuzzy min-max (GFMM) neural network. The agglomerative scheme´s robust behaviour in the presence of noise and outliers and its insensitivity to the order of the training pattern presentation are used as a complementary features to an incremental learning scheme, making it more suitable for online adaptation and dealing with large training data sets
Keywords :
fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); minimax techniques; agglomerative learning algorithm; classification problems; clustering problems; general fuzzy min-max neural network; hyperbox fuzzy sets; incremental learning scheme; large training data sets; noise; online adaptation; outliers; robust behaviour; similarity measures; training pattern presentation order insensitivity; Adaptive systems; Artificial neural networks; Clustering algorithms; Computational intelligence; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Machine learning; Machine learning algorithms; Neural networks;
Conference_Titel :
Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6278-0
DOI :
10.1109/NNSP.2000.890148
