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Conference Papers (Available on Advance Programs) (Sort by: Date Descending)

Committee	Date Time	Place		Paper Title / Authors	Abstract	Paper #
IBISML	2013-03-05 11:00	Aichi	Nagoya Institute of Technology	Non-Achievability of Asymptotic Minimax Regret without Knowledge of the Sample Size Kazuho Watanabe (NAIST), Teemu Roos, Petri Myllymaki (Helsinki Inst. for Information Tech.) IBISML2012-101	The normalized maximum likelihood (NML) model achieves the minimax regret for coding data of fixed sample size $n$. It i... [more]	IBISML2012-101 pp.61-67
PRMU, IBISML, IPSJ-CVIM (Joint) [detail]	2012-09-02 10:30	Tokyo		Detecting Changes of Graph Partitioning Structures using Stochastic Decision Trees Shoichi Sato, Kenji Yamanishi (Univ. of Tokyo) PRMU2012-31 IBISML2012-14	We are concerned with the issue of estimating graph partitioning structures from time series and tracking their changes... [more]	PRMU2012-31 IBISML2012-14 pp.9-16
IBISML	2012-06-19 - 2012-06-20	Kyoto	Campus plaza Kyoto	Detecting changes of graph partitioning structures Shoichi Sato, Kenji Yamanishi (Univ. of Tokyo)	We are concerned with the issue of estimating graph partitioning structures from time series and tracking their changes... [more]
IBISML	2011-11-09 15:45	Nara	Nara Womens Univ.	Detecting Changes of Clustering Structures using Renormalized Maximum Likelihood Coding So Hirai, Kenji Yamanishi (Univ. of Tokyo) IBISML2011-62	Suppose that we sequentially observe multi-dimensional data sets, which are non-stationary. We are concerned with the i... [more]	IBISML2011-62 pp.135-142
IBISML	2011-06-20 16:15	Tokyo	Takeda Hall	Efficient Computation of Re-Normalized Maximum Likelihood Coding for Gaussian Mixtures with Its Applications to Optimal Clustering So Hirai, Kenji Yamanishi (Univ. of Tokyo) IBISML2011-5	We are concerned with the issue of efficient computation of re-normalized maximum likelihood (RNML) code-lengths for Gau... [more]	IBISML2011-5 pp.29-35
IBISML	2011-03-29 11:40	Osaka	Nakanoshima Center, Osaka Univ.	Topic Emergence Detection in Social Networks Using Probabilistic Models of Links Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi (The Univ. of Tokyo) IBISML2010-128	Detection of emerging topics from social network streams is becoming increasingly important these days. Conventional app... [more]	IBISML2010-128 pp.169-176
IBISML	2010-11-05 15:30	Tokyo	IIS, Univ. of Tokyo	[Poster Presentation] Efficient Computation of Normalized Maximum Likelihood Coding for Gaussian Mixtures with Its Applications to Model Selection So Hirai, Kenji Yamanishi (Tokyo Univ.) IBISML2010-103	We are concerned with the issue of efficient computation of normalized maximum likelihood (NML) code-lengths for Gaussia... [more]	IBISML2010-103 pp.327-333
IBISML	2010-06-15 17:15	Tokyo	Takeda Hall, Univ. Tokyo	Graph Clustering based on Normalized Maximum Likelihood Coding So Hirai, Ryota Tomioka, Kenji Yamanishi (Univ. of Tokyo) IBISML2010-27	This paper addresses the issue of graph clustering, i.e., assigning nodes for a given graph into a number of clusters, i... [more]	IBISML2010-27 pp.189-195
NC, MBE (Joint)	2009-03-12 13:50	Tokyo	Tamagawa Univ.	Model Learning of Normalized Gaussian Networks Using On-line Information Bottleneck EM Algorithm Satoshi Imai, Hiroyuki Seki (Nara Inst. of Sci and Tech.) NC2008-143	In this report, we propose a new learning method of stochastic models which have hidden variables. This method estimate... [more]	NC2008-143 pp.237-242
NC	2006-03-16 14:30	Tokyo	Tamagawa University	Application of a Forward-propagation Learning Rule for Adaptive Motor Control with Mixture Models Yoshihiro Ohama, Naohiro Fukumura, Yoji Uno (Toyohashi Univ. Tech.)	We have proposed a forward-propagation learning (FPL) rule for acquiring neural inverse models. FPL can solve a credit a... [more]	NC2005-145 pp.121-126

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