IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2014-06-25 16:10
A Discriminative Model resistant to Malicious Annotators
Atsutoshi Kumagai, Shingo Orihara, Yasushi Okano, Tomoharu Iwata, Yoshihito Oshima (NTT) NC2014-1 IBISML2014-1
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, there have been a lot of studies on learning a classifier from a large amount of labeled data collected from crowds. However, the existing methods have a problem that the accuracy of the classifier drastically deteriorates if there are malicious annotators intentionally giving wrong labels.In this paper, to solve this problem, we propose a method of learning a classifier resistant to malicious annotators by introducing degrees of similarity between discriminative models of annotators. Through experiments, we show the effectiveness of our proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine Learning / multiple annotators / Empirical Bayes method / / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 105, IBISML2014-1, pp. 17-23, June 2014.
Paper # IBISML2014-1 
Date of Issue 2014-06-18 (NC, IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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 NC2014-1 IBISML2014-1

Conference Information
Conference Date 2014-06-25 - 2014-06-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2014-06-NC-BIO-IBISML-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Discriminative Model resistant to Malicious Annotators 
Sub Title (in English)  
Keyword(1) Machine Learning  
Keyword(2) multiple annotators  
Keyword(3) Empirical Bayes method  
1st Author's Name Atsutoshi Kumagai  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Shingo Orihara  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Yasushi Okano  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Tomoharu Iwata  
4th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
5th Author's Name Yoshihito Oshima  
5th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2014-06-25 16:10:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # NC2014-1, IBISML2014-1 
Volume (vol) vol.114 
Number (no) no.104(NC), no.105(IBISML) 
Page pp.17-23 
Date of Issue 2014-06-18 (NC, IBISML) 

[Return to Top Page]

[Return to IEICE Web Page]

The Institute of Electronics, Information and Communication Engineers (IEICE), Japan