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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 #
IBISML 2018-11-05
15:10
Hokkaido Hokkaido Citizens Activites Center (Kaderu 2.7) [Poster Presentation] Crowd Navigation via Bayesian Optimization of Multi-agent Simulation
Hitoshi Shimizu, Takuma Otsuka, Tomoharu Iwata, Hiroshi Sawada, Futoshi Naya, Naonori Ueda (NTT)
In a large event where a large number of people gather, to prepare plans for safely guiding visitors in advance, predict... [more] IBISML2018-57
pp.99-104
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2016-07-06
10:25
Okinawa Okinawa Institute of Science and Technology A Semi-supervised Learning Method for Imbalanced Binary Classification
Akinori Fujino, Naonori Ueda (NTT)
This paper presents a semi-supervised learning method for imbalanced binary classification where the number of positive ... [more] IBISML2016-3
pp.195-200
IBISML 2015-03-06
11:00
Kyoto Kyoto University Asymptotic Properties of Area Under the ROC Curve via Likelihood Ratio Based Ranking Function
Kentaro Nakanishi, Toshiyuki Tanaka (Kyoto Univ.), Naonori Ueda (NTT)
We study properties of the area under the ROC curve, which is one of measures to evaluate performance of a binary classi... [more] IBISML2014-92
pp.55-62
IBISML 2014-11-18
15:00
Aichi Nagoya Univ. [Poster Presentation] Infinitely exchangeable rectangular partitioning
Masahiro Nakano, Katsuhiko Ishiguro, Akisato Kimura, Takeshi Yamada, Naonori Ueda (NTT)
(Advance abstract in Japanese is available) [more] IBISML2014-77
pp.313-320
IBISML 2013-11-13
15:45
Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [Poster Presentation] Floor-plan partitioning stochastic processes for relational data analysis
Masahiro Nakano, Katsuhiko Ishiguro, Akisato Kimura, Takeshi Yamada, Naonori Ueda (NTT)
This paper presents a floor-plan partitioning stochastic process, that can potentially generate arbitrary rectangular pa... [more] IBISML2013-62
pp.197-204
IBISML 2012-11-07
15:30
Tokyo Bunkyo School Building, Tokyo Campus, Tsukuba Univ. Subset Infinite Relational Model
Katsuhiko Ishiguro, Naonori Ueda, Hiroshi Sawada (NTT)
We propose a new probabilistic generative model for analyzing sparse and noisy relational data, such as friend-links on ... [more] IBISML2012-37
pp.23-30
IBISML 2012-03-12
10:50
Tokyo The Institute of Statistical Mathematics Detecting Latent Structural Changes via Latent Dirichlet Allocation
Masashi Ueda, Ryota Tomioka, Kenji Yamanishi (The Univ. of Tokyo), Katsuhiko Ishiguro, Hiroshi Sawada, Naonori Ueda (NTT)
Detecting changes in consumers' latent preference is a fundamental challenge for improving recommendation systems as wel... [more] IBISML2011-89
pp.15-20
NC, MBE
(Joint)
2010-03-09
11:05
Tokyo Tamagawa University Related Abstract Search Tool:RAST
Nilton Liuji Kamiji (RIKEN), Tatsuki Taniguchi (IVIS), Naonori Ueda (NTT Comm Sci Labs.), Shiro Usui (RIKEN)
With the increasing amount of information available in recent years, searching for the desired content is becoming a cha... [more] NC2009-91
pp.23-28
AI 2009-05-22
11:00
Tokyo   Unsupervised extraction of content-related annotations
Tomoharu Iwata, Takeshi Yamada, Naonori Ueda (NTT)
(To be available after the conference date) [more] AI2009-3
pp.13-18
PRMU 2009-03-13
13:25
Miyagi Tohoku Institute of Technology [Special Talk] none
Naonori Ueda (NTT)
 [more] PRMU2008-248
pp.55-60
PRMU, DE 2007-06-29
13:30
Hokkaido Hokkaido Univ. Graph Clustering with a Nonparametric Bayes Model
Shuhei Kuwata, Naonori Ueda, Takeshi Yamada (NTT)
We propose a new graph clustering method based on a nonparametric Bayesian model. Recently, Newman et al. proposed an ef... [more] DE2007-15 PRMU2007-41
pp.81-86
PRMU, DE 2007-06-29
14:00
Hokkaido Hokkaido Univ. Semi-supervised learning based on Dirichlet process mixture models
Naonori Ueda, Takeshi Yamada, Shuhei Kuwata (NTT)
 [more] DE2007-16 PRMU2007-42
pp.87-92
PRMU, DE 2007-06-29
15:45
Hokkaido Hokkaido Univ. Adaptive Parameter Estimation On Multiple-Target Tracking Problem
Katsuhiko Ishiguro, Takeshi Yamada, Naonori Ueda (NTT)
We address a parameter estimation problem for data association in multi-target tracking task.
Many existing methods re... [more]
DE2007-19 PRMU2007-45
pp.105-110
AI 2007-05-31
11:10
Tokyo Kikai-Shinko-Kaikan Bldg. Collaborative Filtering using Purchase Sequences
Tomoharu Iwata, Takeshi Yamada, Naonori Ueda (NTT)
We propose a collaborative filtering method that uses sequential information in purchase histories for recommendations. ... [more] AI2007-3
pp.13-18
AI 2007-05-31
11:35
Tokyo Kikai-Shinko-Kaikan Bldg. Personalized Recommendation by Identifying Innovator
Noriaki Kawamae, Takeshi Yamada, Naonori Ueda (NTT)
(To be available after the conference date) [more] AI2007-4
pp.19-24
PRMU 2006-12-15
10:00
Fukui   A Prediction Algorithm for the Collaborative Filtering Method based on Marginal Rating Distributions
Shuhei Kuwata, Naonori Ueda (NTT)
 [more] PRMU2006-172
pp.7-12
PRMU 2006-12-15
10:30
Fukui   Semi-supervised classification of heterogeneous data
Akinori Fujino, Naonori Ueda, Kazumi Saito (NTT)
 [more] PRMU2006-173
pp.13-18
AI 2006-05-18
10:45
Tokyo Kikai-Shinko-Kaikan Bldg. A collaborative filtering method based on marginal rating distributions
Shuhei Kuwata, Naonori Ueda (NTT)
We propose a collaborative filtering method based on a probabilistic approach . In the proposed method, first empirical ... [more] AI2006-3
pp.13-18
AI 2006-05-18
15:50
Tokyo Kikai-Shinko-Kaikan Bldg. A study of hot topic extraction from document stream based on probabilistic models
Manabu Kimura (NAIST), Kazumi Saito, Naonori Ueda (NTT)
 [more] AI2006-10
pp.51-56
PRMU, NLC 2005-02-24
11:00
Tokyo   Optimal combination of labeled and unlabeled data for semi-supervised classification
Akinori Fujino, Naonori Ueda, Kazumi Saito (NTT)
Unlabeled data are used to improve the accuracy of classifiers when the number of labeled data is not enough. In probabi... [more] NLC2004-100 PRMU2004-182
pp.19-24
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