Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
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AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
A neuro-fuzzy model applied to odor recognition in an artificial nose
Design and application of hybrid intelligent systems
A New Density-Based Scheme for Clustering Based on Genetic Algorithm
Fundamenta Informaticae
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Expert Systems with Applications: An International Journal
Fuzzy associative conjuncted maps network
IEEE Transactions on Neural Networks
Fuzzy velocity-based temporal dependency for SVM-driven realistic facial animation
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1
Expert Systems with Applications: An International Journal
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
Syntactic pattern recognition from observations: a hybrid technique
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
A New Density-Based Scheme for Clustering Based on Genetic Algorithm
Fundamenta Informaticae
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In this paper, we propose two new neuro-fuzzy schemes, one for classification and one for clustering problems. The classification scheme is based on Simpson's fuzzy min-max method (1992, 1993) and relaxes some assumptions he makes. This enables our scheme to handle mutually nonexclusive classes. The neuro-fuzzy clustering scheme is a multiresolution algorithm that is modeled after the mechanics of human pattern recognition. We also present data from an exhaustive comparison of these techniques with neural, statistical, machine learning, and other traditional approaches to pattern recognition applications. The data sets used for comparisons include those from the machine learning repository at the University of California, Irvine. We find that our proposed schemes compare quite well with the existing techniques, and in addition offer the advantages of one-pass learning and online adaptation