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Paper Abstract and Keywords
Presentation 2015-12-19 11:10
A Learning Method of Distributed Support Vector Machine Based on Transfer Control of Data
Yukawa Kiichiro, Mikawa Kenta, Goto Masayuki (Waseda Univ.) AI2015-51
Abstract (in Japanese) (See Japanese page) 
(in English) Due to the development of information technology, the analysis of big data stored in various databases has become more important. From this kind of circumstance, the importance of Distributed Data Mining (DDM), which is the technique to implement data mining while each database doesn't transmit raw data to each other, has been advocated. As one of the methods, Forrero et al. proposed the method of learning optimal support vector machine (SVM) using the Alternating Direction Method of Multipliers (ADMM) in DDM. This method can learn the optimal hyperplane with low iterations and communication costs for any network structure without sharing their data. However, when the statistical characteristics of data stored in each node are quite different, this method requires large iterations until convergence. In this study, we propose a new learning method, which reduces the number of iterations considering network structure, provided that the all nodes are connected with each other. To verify the effectiveness of proposed method, a simulation experiment is conducted.
Keyword (in Japanese) (See Japanese page) 
(in English) Distributed Data Mining / Support Vector Machine / Graph Structure / / / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 381, AI2015-51, pp. 149-154, Dec. 2015.
Paper # AI2015-51 
Date of Issue 2015-12-11 (AI) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF AI2015-51

Conference Information
Committee AI  
Conference Date 2015-12-18 - 2015-12-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To AI 
Conference Code 2015-12-AI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Learning Method of Distributed Support Vector Machine Based on Transfer Control of Data 
Sub Title (in English)  
Keyword(1) Distributed Data Mining  
Keyword(2) Support Vector Machine  
Keyword(3) Graph Structure  
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1st Author's Name Yukawa Kiichiro  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Mikawa Kenta  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Goto Masayuki  
3rd Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2015-12-19 11:10:00 
Presentation Time 20 minutes 
Registration for AI 
Paper # AI2015-51 
Volume (vol) vol.115 
Number (no) no.381 
Page pp.149-154 
#Pages 6 
Date of Issue 2015-12-11 (AI) 

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