With the development of GPRS technology in the field of mobile communication, GPRS has been used in the system of radio data transmission at present. This paper explores the applications of GPRS in the mine monitoring...
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It is widely accepted that cluster ensemble can improve accuracy, stableness and robustness when compared with single cluster approach. As the bagging technique can enhance the prediction accuracy of unstable learning...
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It is widely accepted that cluster ensemble can improve accuracy, stableness and robustness when compared with single cluster approach. As the bagging technique can enhance the prediction accuracy of unstable learning algorithms, and the neural gas algorithm can achieve the structure of datasets, we propose a new structure ensemble framework, named as dual neural gas based structure ensemble with the bagging technique. Experiments on both UCI datasets and synthetic datasets show that tne new framework works well.
With the development of computertechnology and geological modeling study of the gradual maturity, 3D for the engineering geological modeling and information visualization analysis system of opportunities and challeng...
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Notice of Retraction After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles We hereby...
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ISBN:
(纸本)9781457705366
Notice of Retraction After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper. The presenting author of this paper has the option to appeal this decision by contacting TPII@***.
Clustering ensemble is a momentous technique in machine learning and contribute much to the applications in many areas. General clustering ensemble methods pay more attention to predicting cluster labels than structur...
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Clustering ensemble is a momentous technique in machine learning and contribute much to the applications in many areas. General clustering ensemble methods pay more attention to predicting cluster labels than structures of clusters. In fact, learning cluster structures implicates sufficient information to rebuild the dataset and is competent for being the replacement of redundant predicted cluster labels. In this paper, we introduce the fuzzy theory into the structure framework and propose a newfangled double fuzzy c-means structure ensemble framework, named as FCM 2 SE. FCM 2 SE makes use of the cluster structure information instead of predicted labels to gain a representative ensemble structure. We also design two novel labeling criteria to distribute the samples to the corresponding clusters. The empirical results on synthetic datasets and UCI machine learning datasets demonstrate the effectiveness of the proposed method.
The intent of this paper is to analyze and evaluate the impacts of knowledge management for a virtual organization. The rapid developments of Internet technology have accelerated the transformation of traditional busi...
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In this paper we present a pupil location method based on the Laplace operator and region-growing. Firstly, it finds a point in the pupil by gray summing operator for ensuring the beginning point of region-growing in ...
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On the analysis of the current situation of electronic assembly industry, this paper puts forward a manufacturing execution system based on RFID which apply to electronic assembly production process. The implementatio...
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For the well-known reasons, the virus detection schemes based on signature manifest unsatisfactory performance when they dispose the previously unknown virus. Recently, machine learning methods were introduced to buil...
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Automatic analysis of medical cell images has been gaining more importance in pharmacology research. Segmentation of cell images is the crucial step in their analysis since results of segmentation directly affect the ...
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