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Paper Abstract and Keywords
Presentation 2022-12-13 11:05
A Study On the Impact of Network Topology on the Efficiency of Distributed Online Kernel Learning
Koki Takamori, Taichi Emi, Han Nay Aung, Keita Goto, Hiroyuki Ohsaki (Kwansei Univ.) IA2022-58
Abstract (in Japanese) (See Japanese page) 
(in English) Distributed learning, which estimates the parameters of a nonlinear model from nonlinear data observed at each distributed node in a network without aggregating the data at a single location, has been attracting attention.
In particular, by approximating the kernel function using random Fourier features
distributed and online nonlinear learning.
Bouboulis et al. have proposed a distributed online kernel-based learning algorithm RFF-DOKL (Random Fourier Features Distributed Online Kernel-based Learning) using random Fourier features.
On the other hand, the effect of network topology on the efficiency of RFF-DOKL has not been fully clarified.
In this paper, we experimentally investigate the effect of network topology on the efficiency of RFF-DOKL, distributed online kernel-based learning algorithm. Specifically, we experimentally analyze the relationship between total traffic and model accuracy when distributed online learning of nonlinear functions is performed using RFF-DOKL with Gaussian kernels on four different network topologies (series, ring, star, and mesh) with the same number of nodes.
Keyword (in Japanese) (See Japanese page) 
(in English) RFF (Random Fourier Features) / RFF-DOKL (Random Fourier Features Distributed Online Kernel-based Learning) / Kernel-based Learning / Distributed Learning / Network Topology / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 306, IA2022-58, pp. 56-59, Dec. 2022.
Paper # IA2022-58 
Date of Issue 2022-12-05 (IA) 
ISSN 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 IN IA  
Conference Date 2022-12-12 - 2022-12-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Higashi-Senda campus, Hiroshima Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Performance Analysis and Simulation, Robustness, Traffic and Throughput Measurement, Quality of Service (QoS) Control, Congestion Control, Overlay Network/P2P, IPv6, Multicast, Routing, DDoS, etc. 
Paper Information
Registration To IA 
Conference Code 2022-12-IN-IA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study On the Impact of Network Topology on the Efficiency of Distributed Online Kernel Learning 
Sub Title (in English)  
Keyword(1) RFF (Random Fourier Features)  
Keyword(2) RFF-DOKL (Random Fourier Features Distributed Online Kernel-based Learning)  
Keyword(3) Kernel-based Learning  
Keyword(4) Distributed Learning  
Keyword(5) Network Topology  
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Keyword(8)  
1st Author's Name Koki Takamori  
1st Author's Affiliation Kwaisei Gakuin University (Kwansei Univ.)
2nd Author's Name Taichi Emi  
2nd Author's Affiliation Kwaisei Gakuin University (Kwansei Univ.)
3rd Author's Name Han Nay Aung  
3rd Author's Affiliation Kwaisei Gakuin University (Kwansei Univ.)
4th Author's Name Keita Goto  
4th Author's Affiliation Kwaisei Gakuin University (Kwansei Univ.)
5th Author's Name Hiroyuki Ohsaki  
5th Author's Affiliation Kwaisei Gakuin University (Kwansei Univ.)
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Speaker Author-1 
Date Time 2022-12-13 11:05:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2022-58 
Volume (vol) vol.122 
Number (no) no.306 
Page pp.56-59 
#Pages
Date of Issue 2022-12-05 (IA) 


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