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Mostrando ítems 1-8 de 8
Artículo
Spotting Key Members in Networks: Clustering-Embedded Eigenvector Centrality
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020-11-03)
Identifying key members in a social network is critical to understand the underlying system behavior. Whereas there are several measures designed to discern the most central member, they fail to identify a central set of ...
Artículo
Segmentation of scanning-transmission electron microscopy images using the ordered median problem
(Elsevier, 2022-01-15)
This paper presents new models for segmentation of 2D and 3D Scanning-Transmission Electron Micro- scope images based on the ordered median function. The main advantage of using this function is its good adaptability to ...
Artículo
Kernel Penalized K-means: A feature selection method based on Kernel K-means
(ELSEVIER SCIENCE BV, 2015-11-20)
We present an unsupervised method that selects the most relevant features using an embedded strategy while maintaining the cluster structure found with the initial feature set. It is based on the idea of simultaneously ...
Artículo
Variable selection for Naïve Bayes classification
(Pergamon-Elsevier Science Ltd., 2021-07-06)
The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naïve Bayes’ assumption of conditional ...
Artículo
Variable selection for Naïve Bayes classification
(Elsevier, 2021)
The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naïve Bayes’ assumption of conditional i ...
Artículo
New heuristic for harmonic means clustering
(Springer, 2014-05-06)
It is well known that some local search heuristics for K-clustering problems, such as k-means heuristic for minimum sum-of-squares clustering occasionally stop at a solution with a smaller number of clusters than the ...
Artículo
Sum-of-squares clustering on networks
(University of Belgrade, 2011)
Finding p prototypes by minimizing the sum of the squared distances from a set of points to its closest prototype is a well-studied problem in clustering, data analysis and continuous location. In this note, this very ...
Artículo
Clustering categories in support vector machines
(Elsevier, 2016-02)
The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM ...