Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Support Vector Data Description
Machine Learning
The application of DBF neural networks for object recognition
Information Sciences—Informatics and Computer Science: An International Journal
Perception Learning as Granular Computing
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 03
Data compression and harmonic analysis
IEEE Transactions on Information Theory
The application of multiwavelet filterbanks to image processing
IEEE Transactions on Image Processing
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Main purpose of the Granular Computing (GrC) is to find a novel way to acquire knowledge for huge orderless very high dimensional perception information. Obviously, such kind Granular Computing (GrC) has close relationship with machine learning. In this paper, we try to study the machine learning under the point of view of Granular Computing (GrC). Granular Computing (GrC) should contain two parts: (1) dimensional reduction, and (2) information transformation. We proved that although there are tremendous algorithms for dimensional reduction, their ability can't transcend the old fashion wavelet kind nested layered granular computing. To change a high dimensional complex distribution domain to a low dimensional and simple domain is the task of information transformation. We proved that such kind mapping can be achieved as a granular computing by solving a quadric optimization problem.