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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Journal ArticleDOI
Ant colony optimization of clustering models
TL;DR: It is shown how objective function based clustering models such as hard and fuzzy c‐means can be optimized using particular extensions of this simplified ant optimization algorithm.
Journal ArticleDOI
Determination of management zones for a tobacco field based on soil fertility
Wang Xinzhong,Liu Guoshun,Hu Hong-Chao,Wang Zhen-Hai <,Liu Qing-Hua,Liu Xu-Feng,Hao Wei-Hong,Li YanTao +7 more
TL;DR: In this paper, an 87-ha tobacco field was selected to define management zones (MZs) for more precise soil nutrient management for flue-cured tobacco (Nicotiana tabacum) in Central China.
Journal ArticleDOI
A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base
TL;DR: A new safety assessment model for complex system based on the conditional generalized minimum variance (CGMV) and the belief rule base (BRB) is proposed and has shown superior accuracy and less computation complexity.
Journal Article
Future rainfall scenario over Orissa with GCM projections by statistical downscaling
Subimal Ghosh,Pradeep Mujumdar +1 more
TL;DR: A methodology for examining future rainfall scenario using fuzzy clustering technique from the General Circulation Model (GCM) projections, which can model rainfall with a high goodness-of-fit (R2) value.
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.