Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
M-FMCN: modified fuzzy min-max classifier using compensatory neurons
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
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In this paper, we present a modified fuzzy min-max neural network model and its application to feature analysis. In the model a hyperbox can be expanded without considering the hyperbox contraction process as well as the overlapping test. During the learning process, the feature distribution information is utilized to compensate the hyperbox distortion which may be caused by eliminating the overlapping area of hyperboxes in the contraction process. The weight updating scheme and the hyperbox expansion algorithm for the learning process are described. A feature analysis technique for pattern classification using the model is also presented. We define four kinds of relevance factors between features and pattern classes to analyze the saliency of the features in the learning data set.