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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Proceedings ArticleDOI
Optimization of fuzzy clustering criteria using genetic algorithms
TL;DR: This paper introduces a general approach based on genetic algorithms for optimizing a broad class of clustering criteria which re-parameterizes the criteria into functions of the prototype variables alone, and coded as binary strings so that genetic algorithms can be applied.
Journal ArticleDOI
Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics
TL;DR: This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualize system that makes use of both families of algorithms.
Journal ArticleDOI
A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization
TL;DR: This paper presents the development of novel type-2 neuro-fuzzy system for identification of time-varying systems and equalization ofTime-Varying channels using clustering and gradient algorithms, which combines the advantages oftype-2 fuzzy systems and neural networks.
Journal ArticleDOI
A fuzzy neural network to SAR image classification
Y.C. Tzeng,K.S. Chen +1 more
TL;DR: A fuzzy version of a dynamic learning neural network (DL) based on two steps: network representation of fuzzy logic and assignment of membership and Experimental results show that the FDL has faster convergence rate than that of DL and the separability between similar classes is improved.
Journal ArticleDOI
Beetles (Coleoptera) on brownfield sites in England: An important conservation resource?
TL;DR: It is indicated that brownfield sites are important habitats for beetles and there is evidence that the situation is similar for other invertebrate groups and there should be no further assumptions that post-industrial and urban sites have no conservation interest.
References
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Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.