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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering
TL;DR: An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed and finds that the proposed method detectsExudates successfully with sensitivity, specificity, PPV, PLR and accuracy.
Journal ArticleDOI
A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises
TL;DR: A common misunderstanding of Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed to deal with the classification problems with outliers or noises.
Journal ArticleDOI
New clustering algorithm-based fault diagnosis using compensation distance evaluation technique
TL;DR: The diagnosis result shows the algorithm is able to reliably recognise not only different fault categories and severities but also the compound faults, and demonstrates the superior effectiveness and practicability of the algorithm.
Journal ArticleDOI
Traffic accident prediction using 3-D model-based vehicle tracking
TL;DR: A probabilistic model for predicting traffic accidents using three-dimensional (3-D) model-based vehicle tracking is proposed and the effectiveness of the proposed algorithms is shown.
Journal ArticleDOI
Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps
TL;DR: Test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases to show the suitability to improve data management and to easily find coherent clusters between electrical users.
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.