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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Journal ArticleDOI
Development of a systematic methodology of fuzzy logic modeling
TL;DR: A systematic methodology of fuzzy logic modeling for complex system modeling that has a unified parameterized reasoning formulation, an improved fuzzy clustering algorithm, and an efficient strategy of selecting significant system inputs and their membership functions is proposed.
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Advanced structural health monitoring of concrete structures with the aid of acoustic emission
TL;DR: In this article, a comprehensive review of the acoustic emission (AE) technique for its applications in concrete structure health monitoring is presented, with highlights on the limitation of the individual parameter-based approaches when adopted on site.
Journal ArticleDOI
On the principles of fuzzy neural networks
Madan M. Gupta,D. H. Rao +1 more
TL;DR: A fuzzy neural architecture based upon the notion of T-norm and T-conorm connectives is developed, which has the potential to capture the benefits of the two fascinating fields, fuzzy logic and neural networks, into a single capsule.
Journal ArticleDOI
Conditional Fuzzy C-Means
TL;DR: A Fuzzy C-Means-based clustering method guided by an auxiliary (conditional) variable is introduced that reveals a structure within a family of patterns by considering their vicinity in a feature space along with the similarity of the values assumed by a certain conditional variable.
Journal ArticleDOI
A CLUE for CLUster Ensembles
TL;DR: The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions and hierarchies, and facilities for computing on these, including methods for measuring proximity and obtaining consensus and "secondary" clusterings.
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.