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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Improved possibilistic C-means clustering algorithms
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A hybrid sales forecasting system based on clustering and decision trees
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A review on automatic fetal and neonatal brain MRI segmentation.
TL;DR: Methods adopted for the perinatal brain segmentation methods are reviewed and categorised according to the target population, structures segmented and methodology, and future directions and open challenges for research are discussed.
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A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling
TL;DR: Six methods are described that represent different alternatives in the fuzzy modeling process and how they can be integrated with a genetic algorithms to obtain a first approximation to the fuzzy rules without any assumption about the structure of the data.
Proceedings ArticleDOI
Fuzzy intrusion detection
TL;DR: The Fuzzy Intrusion Recognition Engine (FIRE) is a network intrusion detection system that uses fuzzy systems to assess malicious activity against computer networks and can be effective at detecting some types of backdoor and Trojan horse attacks.
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.