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Paper Abstract and Keywords
Presentation 2016-03-25 15:45
Efficient Mondrian Forests by Introducing Supervised Learning
Ryuei Murata (Chubu Univ.), Akisato Kimura, Yoshitaka Ushiku (NTT), Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi (Chubu Univ.) BioX2015-73 PRMU2015-196
Abstract (in Japanese) (See Japanese page) 
(in English) Mondrian Forests is an online learning method based on framework of Random Forests. At the online learning, Mondrian Forests add a node where is judged as necessary for updating by based on difference between trained data and additional data. This makes fast online training of decision trees. Since Mondrian Forests use may label information at the updating, tree structure may contain unnecessary nodes. Therefore, this paper presents an efficient framework of online learning for Mondrian Forests by introducing supervised learning. The proposed method uses label information to design splitting function, and to decide whether additional node is necessary or not. The proposed method can reduce the size of decision tree about 68%, comparing with the conventional method.
Keyword (in Japanese) (See Japanese page) 
(in English) Random Forests / Mondrian Forests / Machine Learning / Online Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 517, PRMU2015-196, pp. 191-196, March 2016.
Paper # PRMU2015-196 
Date of Issue 2016-03-17 (BioX, PRMU) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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 PRMU BioX  
Conference Date 2016-03-24 - 2016-03-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2016-03-PRMU-BioX 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Efficient Mondrian Forests by Introducing Supervised Learning 
Sub Title (in English)  
Keyword(1) Random Forests  
Keyword(2) Mondrian Forests  
Keyword(3) Machine Learning  
Keyword(4) Online Learning  
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1st Author's Name Ryuei Murata  
1st Author's Affiliation Chubu University (Chubu Univ.)
2nd Author's Name Akisato Kimura  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Yoshitaka Ushiku  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Takayoshi Yamashita  
4th Author's Affiliation Chubu University (Chubu Univ.)
5th Author's Name Yuji Yamauchi  
5th Author's Affiliation Chubu University (Chubu Univ.)
6th Author's Name Hironobu Fujiyoshi  
6th Author's Affiliation Chubu University (Chubu Univ.)
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Speaker Author-1 
Date Time 2016-03-25 15:45:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # BioX2015-73, PRMU2015-196 
Volume (vol) vol.115 
Number (no) no.516(BioX), no.517(PRMU) 
Page pp.191-196 
#Pages
Date of Issue 2016-03-17 (BioX, PRMU) 


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