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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 9 of 9  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU 2013-06-10
13:30
Tokyo   Topic Models Taking into Account Burstiness of Local Features in Video
Yang Xie, Koji Eguchi (Kobe Univ.) PRMU2013-20
In this paper we propose a topic model, Corr-DCMLDA, which can integrate visual words and the corresponding speech trans... [more] PRMU2013-20
pp.5-10
SP, EA, SIP 2013-05-16
10:55
Okayama   Permutation-free clustering-based source separation based on time-varying mixture weights
Nobutaka Ito, Shoko Araki, Tomohiro Nakatani (NTT) EA2013-2 SIP2013-2 SP2013-2
To avoid the permutation problem in clustering-based source separation, we introduce a mixture model with time-varying, ... [more] EA2013-2 SIP2013-2 SP2013-2
pp.7-12
IBISML 2011-06-21
14:50
Tokyo Takeda Hall Constructing Dirichlet Forest Priors for Logically Constrained Topic Models
Hayato Kobayashi, Hiromi Wakaki, Tomohiro Yamasaki, Masaru Suzuki (Toshiba) IBISML2011-10
This paper describes a simple method to incorporate logical expressions of term-constraints into Dirichlet forest priors... [more] IBISML2011-10
pp.67-74
IBISML 2011-03-28
11:30
Osaka Nakanoshima Center, Osaka Univ. MPI/OpenMP Hybrid Parallel Inference for Latent Dirichlet Allocation
Shotaro Tora, Koji Eguchi (Kobe Univ.) IBISML2010-118
In recent years, probabilistic topic models have been applied to various kinds of data including text data, and its effe... [more] IBISML2010-118
pp.101-108
IBISML 2010-06-15
09:30
Tokyo Takeda Hall, Univ. Tokyo [Invited Talk] Statistical Machine Learning Based on Nonparametric Bayesian Models
Takaki Makino (Univ. of Tokyo.) IBISML2010-14
Nonparametric Bayesian models are a new approach for machine learning, involving overfitting avoidance and model selecti... [more] IBISML2010-14
pp.87-94
NC, MBE
(Joint)
2010-03-10
13:20
Tokyo Tamagawa University ARMA Model Based Time Series Clustering Using Dirichlet Process Mixture Models
Yuki Washizu, Nobuo Suematsu, Akira Hayashi, Kazunori Iwata (Hiroshima City Univ) NC2009-135
Dirichlet Process Mixture (DPM) models allow nonparametric mixture modeling in which the number of mixture components is... [more] NC2009-135
pp.279-284
PRMU 2009-03-13
10:45
Miyagi Tohoku Institute of Technology [Special Talk] Implementations of Bayesian Learning -- MCMC/SMC/DPEM --
Takashi Matsumoto (Waseda Univ.) PRMU2008-246
Several implementation schemes are reviewed for Bayesian learning. [more] PRMU2008-246
pp.39-42
PRMU 2009-03-13
15:50
Miyagi Tohoku Institute of Technology Semi-supervised learning scheme using Dirichlet process EM-algorithm
Tomoaki Kimura, Yohei Nakada (Waseda Univ.), Arnaud Doucet (ISM), Takashi Matsumoto (Waseda Univ.) PRMU2008-251
Learning with dataset which contains both labeled data and unlabeled data
is often called semi-supervised learning pro... [more]
PRMU2008-251
pp.77-82
PRMU 2009-02-20
10:00
Tokyo Univ. of Tokyo (IIS) Maximum A Posteriori Estimation For Dirichlet Process Language Models
Takaaki Tokuda, Tomoaki Kimura, Yohei Nakada, Takashi Matsumoto (Waseda Univ.) PRMU2008-226
In recent years, Mixture distributions with Dirichlet Process (DP) prior have been successfully applied to many practica... [more] PRMU2008-226
pp.109-114
 Results 1 - 9 of 9  /   
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