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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  2/2016 - 15
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Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View Correlation

ZALL, R. See more information about ZALL, R. on SCOPUS See more information about ZALL, R. on IEEExplore See more information about ZALL, R. on Web of Science, KEYVANPOUR, M. R. See more information about KEYVANPOUR, M. R. on SCOPUS See more information about KEYVANPOUR, M. R. on SCOPUS See more information about KEYVANPOUR, M. R. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,549 KB) | Citation | Downloads: 371 | Views: 1,324

Author keywords
boosting, correlation, classification algorithm, sampling methods, semi-supervised learning

References keywords
semi(17), supervised(16), learning(15), data(15), recognition(11), multi(10), analysis(10), view(9), pattern(9), training(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-05-31
Volume 16, Issue 2, Year 2016, On page(s): 111 - 124
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.02015
Web of Science Accession Number: 000376996100015
SCOPUS ID: 84974853415

Abstract
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Correlated information between different views incorporate useful for learning in multi view data. Canonical correlation analysis (CCA) plays important role to extract these information. However, CCA only extracts the correlated information between paired data and cannot preserve correlated information between within-class samples. In this paper, we propose a two-view semi-supervised learning method called semi-supervised random correlation ensemble base on spectral clustering (SS_RCE). SS_RCE uses a multi-view method based on spectral clustering which takes advantage of discriminative information in multiple views to estimate labeling information of unlabeled samples. In order to enhance discriminative power of CCA features, we incorporate the labeling information of both unlabeled and labeled samples into CCA. Then, we use random correlation between within-class samples from cross view to extract diverse correlated features for training component classifiers. Furthermore, we extend a general model namely SSMV_RCE to construct ensemble method to tackle semi-supervised learning in the presence of multiple views. Finally, we compare the proposed methods with existing multi-view feature extraction methods using multi-view semi-supervised ensembles. Experimental results on various multi-view data sets are presented to demonstrate the effectiveness of the proposed methods.


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References Weight

Web of Science® Citations for all references: 10,253 TCR
SCOPUS® Citations for all references: 14,282 TCR

Web of Science® Average Citations per reference: 218 ACR
SCOPUS® Average Citations per reference: 304 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-12-16 10:28 in 282 seconds.




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