论文信息 - General fuzzy hyperline segment neural network

General fuzzy hyperline segment neural network

We propose a general fuzzy hyperline segment neural network (GFHLSNN) and its learning algorithm, which is an extension of the fuzzy hyperline segment neural network proposed by Kulkarni et al (2001). It combines supervised and unsupervised learning in a single algorithm so that it can be used for pure classification, pure clustering and hybrid classification/clustering.

T. R. Sontakke | Pradeep M. Patil | U. V. Kulkarni

[1] Paulo J. G. Lisboa,et al. Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers , 1992, IEEE Trans. Neural Networks.

[2] Andrzej Bargiela,et al. General fuzzy min-max neural network for clustering and classification , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3] Witold Pedrycz,et al. Fuzzy clustering with partial supervision , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[4] Din-Chang Tseng,et al. Invariant handwritten Chinese character recognition using fuzzy min-max neural networks , 1997, Pattern Recognit. Lett..

[5] T. R. Sontakke,et al. Fuzzy hyperline segment neural network for rotation invariant handwritten character recognition , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[6] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .