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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment
Subimal Ghosh,Pradeep Mujumdar +1 more
TL;DR: In this paper, a methodology is developed to address the uncertainties for a specific problem of drought impact assessment with results from GCM simulations, where samples of a drought indicator are generated with downscaled precipitation from available GCMs and scenarios.
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Empirical estimates of uncertainty for mapping continuous depth functions of soil attributes
TL;DR: In this paper, the authors use an empirical method where model output uncertainties are expressed as a prediction interval (PI) of the underlying distribution of prediction errors, which obviates the need to identify and determine the contribution of each source of uncertainty to the overall prediction uncertainty.
Journal ArticleDOI
Neural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain)
TL;DR: In this paper, three different NNs were developed: one including the whole data set, and two others using only points belonging to one of the clusters, and the input data for these latter two NNs was chosen depending on the quality level defined on the basis of quality flags given to each data set.
Journal ArticleDOI
A weighted sum validity function for clustering with a hybrid niching genetic algorithm
TL;DR: An objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions, is suggested, which is generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.
Journal ArticleDOI
Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering
TL;DR: This study presents a two-level SOFM-based clustering approach for regionalization of watersheds, and finds that previous indices used to decide the number of clusters are not efficient, and suggests a new index that is more appropriate for this purpose.
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.