IEICE Technical Committee Submission System
Conference Schedule
Online Proceedings
[Sign in]
Tech. Rep. Archives
    [Japanese] / [English] 
( Committee/Place/Topics  ) --Press->
 
( Paper Keywords:  /  Column:Title Auth. Affi. Abst. Keyword ) --Press->

All Technical Committee Conferences  (Searched in: All Years)

Search Results: Conference Papers
 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 4 of 4  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2018-11-05
15:10
Hokkaido Hokkaido Citizens Activites Center (Kaderu 2.7) [Poster Presentation] Inference for Logitistic Regression Mixture Model with Local Variational Approximation and Study for Variational Free Energy
Fumito Nakamura, Ryosuke Konishi (Generic Solution), Yasushi Kiyoki (Keio) IBISML2018-48
A logistic regression mixture model (LRMM) is a mixed model of the Logistic regression model, and it is widely used in t... [more] IBISML2018-48
pp.29-36
IBISML 2014-11-17
17:00
Aichi Nagoya Univ. [Poster Presentation] Theoretical Analysis of Empirical MAP and Empirical Partially Bayes
Shinichi Nakajima (TU Berlin), Masashi Sugiyama (Univ. of Tokyo) IBISML2014-38
Variational Bayesian (VB) learning is known to be a
promising
approximation to Bayesian learning
with computational... [more]
IBISML2014-38
pp.25-32
NC 2011-07-25
13:45
Hyogo Graduate School of Engineering, Kobe University General Framework for Local Variational Approximation of Bayesian Learning Using Bregman Divergence
Kazuho Watanabe (NAIST), Masato Okada (Univ. of Tokyo), Kazushi Ikeda (NAIST) NC2011-25
The local variational method is a technique to approximate an intractable posterior distribution in Bayesian learning. T... [more] NC2011-25
pp.25-30
NC, MBE
(Joint)
2010-03-10
14:35
Tokyo Tamagawa University Information Divergences in Local Variational Approximation of Bayesian Posterior Distribution
Kazuho Watanabe (NAIST), Masato Okada (Univ. of Tokyo), Kazushi Ikeda (NAIST) NC2009-138
Local variational method is a technique to approximate intractable posterior distributions in Bayesian learning.
In thi... [more]
NC2009-138
pp.297-302
 Results 1 - 4 of 4  /   
Choose a download format for default settings. [NEW !!]
Text format pLaTeX format CSV format BibTeX format
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)


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan