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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Journal ArticleDOI
Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment
TL;DR: A novel method of HRV analysis for mental stress assessment using fuzzy clustering and robust identification techniques and a robust fuzzy identification technique has been used to handle the uncertainties due to individual variations for the assessment of mental stress.
Journal ArticleDOI
A survey of defuzzification strategies
TL;DR: The merits and demerits of various defuzzification strategies that are used in the theory and practice, and in design and implementation of applications involving fuzzy theory, fuzzy control, and fuzzy rule base, and furry inference‐based systems are presented.
Journal ArticleDOI
Spatial classification using fuzzy membership models
John T. Kent,Kanti V. Mardia +1 more
TL;DR: Two models based on multivariate Gaussian random fields are proposed to model this fuzzy membership process of mixed-pixel data, which involves predicting the group membership and estimating the parameters.
Journal ArticleDOI
Fuzzy clustering for symbolic data
TL;DR: A fuzzy symbolic c-means algorithm is introduced as an application of applying and testing the proposed algorithm on real and synthetic data sets and the results show that the new technique is quite efficient and superior to traditional methods of hierarchical nature.
Dissertation
Particle swarm optimization methods for pattern recognition and image processing
TL;DR: This thesis investigates the application of an efficient optimization method, known as Particle Swarm Optimization (PSO), to the field of pattern recognition and image processing and proposes three approaches to tackle the color image quantization and spectral unmixing problems.
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.