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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area
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Self-splitting competitive learning: a new on-line clustering paradigm
Ya-Jun Zhang,Zhi-Qiang Liu +1 more
TL;DR: A new, more powerful competitive learning algorithm, self-splitting competitive learning (SSCL), that is able to find the natural number of clusters based on the one-prototype-take-one-cluster (OPTOC) paradigm and a self- Splitting validity measure is presented.
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Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method
TL;DR: In this paper, the authors proposed a multi-step forecasting method based on a Cuckoo search (CS) optimized fuzzy clustering, and an Apriori association process.
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Fuzzy classifier design using genetic algorithms
Enwang Zhou,Alireza Khotanzad +1 more
TL;DR: A new method for design of a fuzzy-rule-based classifier using genetic algorithms (GAs) is discussed, and results indicate that highly accurate classifiers could be designed with relatively few fuzzy rules.
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Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.
Javier Juan-Albarracín,Elies Fuster-Garcia,José V. Manjón,Montserrat Robles,Fernando Aparici,Luis Martí-Bonmatí,Juan M. García-Gómez +6 more
TL;DR: An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations to confirm the method as a viable alternative for brain tumour segmentation.
References
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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.