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Paper Abstract and Keywords
Presentation 2019-12-06 13:25
Interpretation of Multi-Label Learning by combining two probability models -- An approach that interprets evaluate texts by regarding labels as teacher data --
Kurebayashi Kosuke, Morizumi Tetsuya, Kinoshita Hirotsugu (Kanagawa Univ.) SITE2019-81
Abstract (in Japanese) (See Japanese page) 
(in English) We have already proposed a security model with a three-layer structure of AI architecture. However, there is a certain lack of certainty whether a security policy or the like determined by humans can be reflected in access control even if it is included in teacher data. In this paper, the weighting in Multi-label Learning is separated from the probabilistic model for the purpose of surely reflecting the security policy and security rules newly established by humans, and the tensor components (words) composed of labels and topics are separated. On the other hand, we propose a model weighted manually.
The manual [label, topic] tensor weighting is obtained by extracting a label latent random variable and a topic latent random variable tensor defined by multi-label learning from a multi-label learning probability model. In this proposal, the [Label, Topic] tensor is extracted from the Multi Label probabilistic model, so that the entire probabilistic model is separated into two probabilistic models (LDA). These correspond to the first and second layers of the three-layer AI architecture, respectively.
By performing this weighting, it is possible to reliably reflect the security policy to what is intended by humans. In this paper, we confirmed by experiment how much utility the weighting can be obtained.
Keyword (in Japanese) (See Japanese page) 
(in English) Artificial intelligence / Machine learning / Access control / Probabilistic security model / Probabilistic model / LDA / Tensor Decomposition /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 329, SITE2019-81, pp. 7-12, Dec. 2019.
Paper # SITE2019-81 
Date of Issue 2019-11-29 (SITE) 
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)
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Conference Information
Committee SITE  
Conference Date 2019-12-06 - 2019-12-06 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SITE 
Conference Code 2019-12-SITE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Interpretation of Multi-Label Learning by combining two probability models 
Sub Title (in English) An approach that interprets evaluate texts by regarding labels as teacher data 
Keyword(1) Artificial intelligence  
Keyword(2) Machine learning  
Keyword(3) Access control  
Keyword(4) Probabilistic security model  
Keyword(5) Probabilistic model  
Keyword(6) LDA  
Keyword(7) Tensor Decomposition  
Keyword(8)  
1st Author's Name Kurebayashi Kosuke  
1st Author's Affiliation Kanagawa University (Kanagawa Univ.)
2nd Author's Name Morizumi Tetsuya  
2nd Author's Affiliation Kanagawa University (Kanagawa Univ.)
3rd Author's Name Kinoshita Hirotsugu  
3rd Author's Affiliation Kanagawa University (Kanagawa Univ.)
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Speaker Author-1 
Date Time 2019-12-06 13:25:00 
Presentation Time 25 minutes 
Registration for SITE 
Paper # SITE2019-81 
Volume (vol) vol.119 
Number (no) no.329 
Page pp.7-12 
#Pages 6 
Date of Issue 2019-11-29 (SITE) 

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