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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Journal ArticleDOI
Fuzzy clustering
TL;DR: It is demonstrated that the use of membership degrees for fuzzy clustering algorithms - although it is not necessary from the theoretical point of view - is essential for these algorithms to function in practice.
Journal ArticleDOI
Fuzzy Learning Decomposition for the Scheduling of Hydroelectric Power Systems
TL;DR: A nonlinear multivariable fitting model to decompose the optimal policies obtained by dynamic programming of a unique aggregated reservoir is presented and the CPU time is reduced by a factormore of 15 to 20 compared with the back propagation technique.
Journal ArticleDOI
Fuzzy logic approaches to structure preserving dimensionality reduction
N.R. Pal,V.K. Eluri,G.K. Mandal +2 more
TL;DR: A low-cost fuzzy rule-based implementation of Sammon's method for structure preserving dimensionality reduction and these schemes are found to be quite effective to project new points, i.e., such systems have good predictability.
Book ChapterDOI
Fuzzy Set Techniques in Information Retrieval
TL;DR: The chapter presents the fuzzy associative retrieval models based on thesauri, pseudothesauri, and documents clustering and relevance feedback techniques and some evaluation issues of IRSs are introduced.
Journal ArticleDOI
Local convergence of the fuzzy c-Means algorithms
TL;DR: A local convergence property is proved which shows that whenever an FCM algorithm is started sufficiently near a minimizer of the corresponding objective function, then the iteration sequence must converge to that particular minimizer.
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.