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All Technical Committee Conferences (Searched in: All Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
PRMU |
2013-06-10 13:30 |
Tokyo |
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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 |
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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 |
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