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Paper Abstract and Keywords
Presentation 2019-01-24 15:20
An Error Correction Technique with the Viterbi Algorithm for a Machine-Learning-Based Classifier
Yoshito Watanabe, Yozo Shoji (NICT) SR2018-105
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a novel technique to correct errors that can be caused in the decisions made by a machine learning (ML)-based classifier. Considering the log-likelihood ratio (LLR) of the posterior probability emitted from a classifier for each class as the observed variable, a hidden Markov model (HMM) is introduced to numerically model the sequential operation of classification process, and the Viterbi algorithm is then applied to the sequence of the LLR to make the final decisions. A case of binary classification, in which mobile-sensing-based anomaly detection employing a support vector machine (SVM), is first investigated. The method is extended to an example case of multiclass classification, in which an application of human activity recognition from the acceleration data obtained by body-worn smartphones by a neural network is demonstrated. The simulation results show that the proposed technique is effective for correcting the misclassifications made by conventional ML-based classifiers.
Keyword (in Japanese) (See Japanese page) 
(in English) machine learning / sequential classification / the Viterbi algorithm / hidden Markov model / the EM algorithm / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 421, SR2018-105, pp. 57-61, Jan. 2019.
Paper # SR2018-105 
Date of Issue 2019-01-17 (SR) 
ISSN 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 SR2018-105

Conference Information
Committee SR  
Conference Date 2019-01-24 - 2019-01-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Corasse, Fukushima city (Fukushima prefecture) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) cognitive radio, machine learning application, heterogeneous network, SDN, IoT etc. 
Paper Information
Registration To SR 
Conference Code 2019-01-SR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Error Correction Technique with the Viterbi Algorithm for a Machine-Learning-Based Classifier 
Sub Title (in English)  
Keyword(1) machine learning  
Keyword(2) sequential classification  
Keyword(3) the Viterbi algorithm  
Keyword(4) hidden Markov model  
Keyword(5) the EM algorithm  
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1st Author's Name Yoshito Watanabe  
1st Author's Affiliation National Institute of Information and Communications Technology (NICT)
2nd Author's Name Yozo Shoji  
2nd Author's Affiliation National Institute of Information and Communications Technology (NICT)
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Speaker Author-1 
Date Time 2019-01-24 15:20:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2018-105 
Volume (vol) vol.118 
Number (no) no.421 
Page pp.57-61 
#Pages 5 
Date of Issue 2019-01-17 (SR) 

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