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General fuzzy min-max neural network for clustering and classification [PDF]
, 2000 This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993).
Gabrys, Bogdan, Bargiela, Andrzej
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Semi-automated counting model for arbuscular mycorrhizal fungi spores using the Circle Hough Transform and an artificial neural network [PDF]
Anais da Academia Brasileira de Ciências: Arbuscular Mycorrhizae (AM) are mutualistic associations between Arbuscular Mycorrhizal Fungi (AMF) and the roots of many plant species. AMF spores give rise to filaments that develop in the root system of plants and contribute to the absorption of ...
CLÊNIA A.O. DE MELO +4 more
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Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition [PDF]
International Journal of Computational Intelligence Systems, 2013 Many previous studies have shown that class classification can be greatly improved by kernel Fisher discriminant analysis (KDA) technique. However, KDA only captures global geometrical structure and disregards local geometrical structure of the data.
Di Zhang, Jiazhong He, Yun Zhao
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Multi‐model fusion of classifiers for blood pressure estimation
IET Systems Biology, 2021 Prehypertension is a new risky disease defined in the seventh report issued by the Joint National Commission. Hence, detecting prehypertension in time plays a very important role in protecting human lives.
Qi Ye +4 more
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Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs
Remote Sensing, 2018 Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify
Feng Gao, Qun Wang, Junyu Dong, Qizhi Xu
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Multicriteria Classifier Ensemble Learning for Imbalanced Data
IEEE Access, 2022 One of the vital problems with the imbalanced data classifier training is the definition of an optimization criterion. Typically, since the exact cost of misclassification of the individual classes is unknown, combined metrics and loss functions that ...
Weronika Wegier +2 more
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Sparse Coefficient-Based ${k}$ -Nearest Neighbor Classification
IEEE Access, 2017 K-nearest neighbor rule (KNN) and sparse representation (SR) are widely used algorithms in pattern classification. In this paper, we propose two new nearest neighbor classification methods, in which the novel weighted voting methods are developed for ...
Hongxing Ma +4 more
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Frontiers in Neuroscience, 2018 Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to ...
Xiangfei Geng +5 more
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Abstraction and pattern classification
Journal of Mathematical Analysis and Applications, 1966 Abstract : This is a preliminary paper in which the authors discuss a general framework for the treatment of pattern-recognition problems. They make precise the notion of a 'fuzzy' set. Then they show how this may be employed in a sequential experimental procedure to ascertain whether a symbol is a member of a particular set or not.
Bellman, R, Kalaba, R, Zadeh, L
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L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets [PDF]
, 2022 Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine
Ilknur Tuncer +27 more
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