Algebraic feature extraction of image for recognition
Pattern Recognition
Handbook of pattern recognition & computer vision
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Rough Neural Network of Variable Precision
Neural Processing Letters
Soft data mining, computational theory of perceptions, and rough-fuzzy approach
Information Sciences: an International Journal - Special issue: Soft computing data mining
Rough set spatial data modeling for data mining
International Journal of Intelligent Systems - Granular Computing and Data Mining
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
Approximate boolean reasoning approach to rough sets and data mining
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
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Based on bottom-up fuzzy rough data analysis, a new rough neural network decision-making model is proposed. Through supervised Gaustafason---Kessel (G---K) clustering algorithm, proper fuzzy clusters are found to partition the input data space. At the same time cluster number is searched by monotone increasing process. If the cluster number matches with that exactly exist in data sets then excellent fuzzy rough data modeling (FRDM) model can be built. And by integrating it with neural network technique, corresponding rough neural network is constructed. Our method overcomes the defects of conventional top-down based rough logic neural network (RLNN) method, and it also achieves adaptive learning ability and comprehensive soft decision-making ability compared with FRDM model. The experiment results indicate that our method has stronger generalization ability and more compact network structure than conventional RLNN.