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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Journal ArticleDOI
Rough–Fuzzy Collaborative Clustering
TL;DR: A novel clustering architecture is introduced, in which several subsets of patterns can be processed together with an objective of finding a common structure, and the required communication links are established at the level of cluster prototypes and partition matrices.
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Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches
Xavier Lladó,Arnau Oliver,Mariano Cabezas,Jordi Freixenet,Joan C. Vilanova,Ana Quiles,Laia Valls,Lluís Ramió-Torrentí,ílex Rovira +8 more
TL;DR: The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions.
Journal ArticleDOI
Fuzzy controllers: synthesis and equivalences
Sylvie Galichet,Laurent Foulloy +1 more
TL;DR: The aim of this paper is to show how to automatically build this fuzzy controller, and the proposed design methodology is detailed for the synthesis of a Sugeno or Mamdani type fuzzy controller precisely equivalent to a given PI controller.
Journal ArticleDOI
Node Detection Using High-Dimensional Fuzzy Parcellation Applied to the Insular Cortex
Ugo Vercelli,Matteo Diano,Tommaso Costa,Andrea Nani,Sergio Duca,Giuliano Geminiani,Alessandro Vercelli,Franco Cauda +7 more
TL;DR: A relatively simple “one-step” border detection and ROI estimation procedure employing the fuzzy c-mean clustering algorithm that revealed a complex pattern of functional connectivity where the two previously detected insular clusters are subdivided into several other networks, some of which are not commonly associated with the insular cortex.
Journal ArticleDOI
Fuzzy-wavelet rbfnn model for freeway incident detection
Hojjat Adeli,Asim Karim +1 more
TL;DR: A new multiparadigm intelligent system approach is presented for the solution of the incident detection problem, employing advanced signal processing, pattern recognition, and classification techniques and produced excellent incident detection rates with no false alarms when tested using both real and simulated data.
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.