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) |
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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) |
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machine learning |
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sequential classification |
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the Viterbi algorithm |
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hidden Markov model |
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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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