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Paper Abstract and Keywords
Presentation 2017-11-09 13:00
Regression Method for Noisy Inputs based on Naradaya-Watson Estimator constructed from Noiseless Training Data
Ryo Hanafusa, Takeshi Okadome (Kwansei Gakuin Univ.) IBISML2017-46
Abstract (in Japanese) (See Japanese page) 
(in English) The regression method proposed in this paper produces a regression function for noisy inputs that minimizes the expected squared loss in prediction. Given a noisy input, ${bf u}$ $=$ ${bf x}$ $+$ ${bm eta}$, where ${bm eta}$ is noise and ${bf x}$ is the noise-free constituent of ${bf u}$, the proposed method estimates the posterior, $p({bf x}|{bf u})$, and represents it by the noise distribution, $p({bm eta})$. The method produces the expected value (on $p({bf x}|{bf u})$) of the Nadaraya--Watson estimator for noiseless input. The model enables us to determine $p({bf x}|{bf u})$ parametrically from a single noisy input, ${bf u}$, using a hidden Markov model that represents a time series given as noiseless training data. Experiments conducted using artificial and real datasets show that the method suppresses the overfitting of the regression function for noisy inputs and the RMSEs of the predictions are smaller compared with those of an existing method.
Keyword (in Japanese) (See Japanese page) 
(in English) noisy input / Nadaraya-Watson estimator / minimum expected squared loss / noiseless training data / hidden Markov model / parameter estimation / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-46, pp. 85-92, Nov. 2017.
Paper # IBISML2017-46 
Date of Issue 2017-11-02 (IBISML) 
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 IBISML  
Conference Date 2017-11-08 - 2017-11-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Univ. of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2017) 
Paper Information
Registration To IBISML 
Conference Code 2017-11-IBISML 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Regression Method for Noisy Inputs based on Naradaya-Watson Estimator constructed from Noiseless Training Data 
Sub Title (in English)  
Keyword(1) noisy input  
Keyword(2) Nadaraya-Watson estimator  
Keyword(3) minimum expected squared loss  
Keyword(4) noiseless training data  
Keyword(5) hidden Markov model  
Keyword(6) parameter estimation  
Keyword(7)  
Keyword(8)  
1st Author's Name Ryo Hanafusa  
1st Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
2nd Author's Name Takeshi Okadome  
2nd Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
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Speaker Author-1 
Date Time 2017-11-09 13:00:00 
Presentation Time 150 minutes 
Registration for IBISML 
Paper # IBISML2017-46 
Volume (vol) vol.117 
Number (no) no.293 
Page pp.85-92 
#Pages 8 
Date of Issue 2017-11-02 (IBISML) 

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