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Committee Date Time Place Paper Title / Authors Abstract Paper #
NC 2012-01-26
15:15		 Hokkaido Future University Hakodate The learning theory and algorithm of latent multi-dynamical systems -- Implementation by higher-order topographic mapping --
Tetsuo Furukawa, Takashi Ohkubo (Kyutech) NC2011-107		 The purpose of this paper is to establish the learning theory of multiple dynamical systems, as well as to develop
the ... [more]		 NC2011-107
pp.59-64
IBISML 2011-11-09
15:45		 Nara Nara Womens Univ. An Accuracy Analysis of Latent Variable Estimation with the Maximum Likelihood Estimator
Keisuke Yamazaki (Tokyo Inst. of Tech.) IBISML2011-55		 Hierarchical learning models such as
mixture models and hidden Markov models
are widely used in machine learning and d... [more]		 IBISML2011-55
pp.87-91
IBISML 2011-11-10
15:45		 Nara Nara Womens Univ. Image Segmentation and Restoration using Switching State-Space Model and Variational Bayesian Method
Ryota Hasegawa (Kansai Univ.), Ken Takiyama, Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) IBISML2011-67		 We derive a deterministic algorithm that restores and segments image using switching state-space model and variational B... [more] IBISML2011-67
pp.169-174
IBISML 2011-11-10
15:45		 Nara Nara Womens Univ. Image segmentation and restoration by variational Bayesian method and MCMC
Kenta Kayano (Kansai Univ.), Kenji Nagata, Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) IBISML2011-68		 In this paper, we derive a deterministic algorithm that restores and segments an image by using variational Bayesian met... [more] IBISML2011-68
pp.175-180
IBISML 2010-11-04
15:00		 Tokyo IIS, Univ. of Tokyo [Poster Presentation] Image Segmentation by Region-Based Latent Variables and Belief Propagation
Ryota Hasegawa, Seiji Miyoshi (Kansai Univ.), Masato Okada (Univ. of Tokyo) IBISML2010-71		 To represent edges in image processing based on Bayesian inference, it is very effective to introduce latent variables. ... [more] IBISML2010-71
pp.91-97
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