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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Sub-pixel Target Mapping from Soft-classified, Remotely Sensed Imagery
TL;DR: In this article, a simple, efficient algorithm is presented for sub-pixel target mapping from remotely-sensed images, which works in a series of iterations, each of which contains three stages.
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Classification by fuzzy integral: performance and tests
TL;DR: An introductory explanation about the approach is given, a lower bound of the minimal number of training samples is found, and it is shown that a minimum squared error criterion leads to the best approximate for the optimal Bayes classifier.
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A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm
TL;DR: A fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm yields sufficient and sensible imputation performance results.
Journal ArticleDOI
Distributed $k$ -Means Algorithm and Fuzzy $c$ -Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory
TL;DR: Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.
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.