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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Journal ArticleDOI
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
TL;DR: This article compares k-mean to fuzzy c-means and rough k-Means as important representatives of soft clustering, and surveys important extensions and derivatives of these algorithms.
Journal ArticleDOI
DArT for high-throughput genotyping of Cassava (Manihot esculenta) and its wild relatives.
Ling Xia,Kaiman Peng,Shiying Yang,Peter Wenzl,M. Carmen De Vicente,Martin A. Fregene,Andrzej Kilian +6 more
TL;DR: This study suggests that DArT offers advantages over current technologies in terms of cost and speed of marker discovery and analysis and can therefore be used to genotype large germplasm collections.
Journal ArticleDOI
Study of pharmaceutical samples by NIR chemical-image and multivariate analysis
TL;DR: The possibilities of different algorithms in the global study of homogeneity in pharmaceutical samples that may confirm different stages in a blending process are explored, and new possibilities in cluster analysis and MCR-ALS in image analysis are presented.
Proceedings ArticleDOI
A divide et impera strategy for automatic classification of retinal vessels into arteries and veins
Enrico Grisan,Alfredo Ruggeri +1 more
TL;DR: A new algorithm for classifying the vessels is developed, which exploits the peculiarities of retinal images, and a divide et impera approach that partitioned a concentric zone around the optic disc into quadrants was able to perform a more robust local classification analysis.
Journal Article
Image Segmentation by Fuzzy C-Means Clustering Algorithm with a Novel Penalty Term
Yong Yang,Shuying Huang +1 more
TL;DR: To overcome the noise sensitiveness of conventional fuzzy c-means (FCM) clustering algorithm, a novel extended FCM algorithm for image segmentation is presented in this paper, which is inspired from the neighborhood expectation maximization algorithm.
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.