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論文(査読有りま たは招待)および著書

  1. ジャーナル論文
  2. 国際会議論文
  3. 招待解説論文・記事
  4. NTT機関論文誌および研究実用化報告
  5. 登録特許
  6. 著書・訳書
ジャーナル論文
  1. Iwata, T., Shimizu, H., Naya, F. and Ueda, N., "Estimating people flow from spatio-temporal population data via collective graphical mixture models," ACM Transactions on Spatial Algorithms and Systems, Vol. 3, Issue 1, Article 39. 2017.

  2. Ishiguro, K., Sato, I. and Ueda, N., "Averaged collapsed variational Bayes inference," Journal of Machine Learning Resaerch (JMLR), Volume 18, Number 1, pp.1--29, 2017.

  3. 上田修功, "時空間予測技術に基づく先行的集団最良誘導," 応用統計学(招待論文), Vol.45, No.3, pp.89-104, 2016.

  4. Morii, M., Ikeda, S., Tominaga, N., Tanaka, M., Morokuma, T., Ishiguro, K., Yamato, J., Ueda, N., Suzuki, N., Yasuda, N. and Yoshida, N., "Machine-learning selection of optical transients in Subaru/hyper suprime-cam survey," Publication of Astronomical Society of Japan, Oxford University press, 2016.(to appear)

  5. Inoue, S., Ueda, N., Nohara, Y. and Nakashima, N., "Recognizing and understanding nursing activities for a whole day with a big data set," Journal of Information Processing, to appear, 2016.

  6. 上田修功,"時空間予測技術とその先行的人流誘導への応用,"画像電子学会誌(招待論文), Vol.45, No.1, pp.4-11, 2016.

  7. Iwata, T., Hirao T. and Ueda, N., " Unsupervised many-to-many object matching via probabilistic latent variable models,"Information Processing & Management, Volume 52, Issue 4, pp682-697, July 2016.

  8. Blondel, M., Onogi, A., Iwata, H. and Ueda, N., "A Ranking Approach to Genomic Selection,"PLOS ONE (peer-reviewed open acces journal), Public Library of Science, 2015.

  9. Nohara, Y., Kai, E., Ghosh, P., Islam, R., Ahmed, A., Kuroda, M., Inoue, S., Hiramatsu, T., Kimura, M., Shimizu, S., Kobayashi, K., Baba, Y., Kashima, H., Tsuda, K., Sugiyama, M., Blondel, M., Ueda, N., Kitsuregawa, M. and Nakashima, N., "Health Checkup and Telemedical Intervention Program for Preventive Medicine in Developing Countries: Verification Study," Journal of Medical Internet Research, Vol.17, No.1 January 2015.

  10. 田中祐典,上田修功.田中利幸, "クラス固有の特徴選択に基づくベイズ分類器," 電子情報通信学会和文論文誌(D-II), Vol.J96-D, No.11, pp.2755-2764, 2013.

  11. Sun, X. Kashima, H. and Ueda, N., "Large-Scale Personalized Human Activity Recognition using Online Multi-Task Learning, "IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol.25, No.11, pp.2551-2563, 2013. [IEEE Copyright Notice]

  12. Sawada, H., Kameoka, H., Araki, S. and Ueda, N., "Multichannel Extensions of Non-negative Matrix Factorization with Complex-valued Data," IEEE Transactions on Audio, Speech, and Language Processing, Vol.21, No.5, pp.971-982, 2013. [IEEE Copyright Notice]

  13. Iwata, T., Yamada, T. and Ueda, N., "Modeling Noisy Annotated Data with Application to Social Annotation," IEEE Transactions on Knowledge and Data Engineering, Vol.25, No.7, pp.1601-1613, 2013. [IEEE Copyright Notice]

  14. Fujino, A., Ueda, N. and Nagata, M., "Adaptive semi-supervised learning on labeled and unlabeled data with different distributions," Knowledge and Information Systems(KAIS), Vol. 37, Issue 1, pp. 129-154, Springer, 2013, (invited paper).

  15. Iwata, T., Yamada, T., SakuraI, Y. and Ueda, N., "Sequential Modeling of Topics Dynamics with Multiple Timiscales," ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 5 Issue 4, 19:1-19:27, 2012.

  16. Hachiya, H., Sugiyama, M. and Ueda, N., "Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition," Neurocomputing, Vol. 80, pp 93-101, 2012.

  17. 藤野昭典, 上田修功, 永田昌明, "ラベルありデータの選択バイアスに頑健な 半教師あり学習," 情報処理学会論文誌, Vol.4, No.2, pp. 31-42, 2011.

  18. Iwata, T., Tanaka, T., Yamada, T. and Ueda, N., "Improving Classifier Performance Using Data with Different Taxonomies," IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol.23, No.11, 1668-1677, 2011.[IEEE Xplore] [IEEE Copyright Notice],

  19. 藤野昭典, 上田修功, 永田昌明, "ラベルありデータの選択バイアスに頑健な半教師あり学習," 情報処理学会論文誌  数理モデル化と応用, Vol.2010-MPS-80 No.8, 2010.

  20. 石黒 勝彦, 岩田具治, 上田修功, "時間依存関係データ分析のための動的無限関係モデル,"情報処理学会論文誌 数理モデル化と応用, Vol.3, No.1, pp. 1-12, 2010.

  21. 岩田具治, 渡部晋治, 山田武士, 上田修功, "購買行動解析のためのトピック追跡モデル," 電子情報通信学会, Vol.J93-D, No.6, pp.978-987, 2010

  22. 岩田具治, 田中利幸, 山田武士, 上田修功, "分布が変化するデータにおけるモデル学習法," 電子情報通信学会, Vol.J-92D, No.3, 361-370, 2009.

  23. 岩田具治, 山田武士, 上田修功, "トピックモデルに基づく文書群の可視化," 情報処理学会論文誌, Vol.50, No.6, 1649-1659, 2009.

  24. 川前徳章, 坂野 鋭, 山田武士, 上田修功, "ユーザの嗜好の時系列性と先行性に着目した協調フィルタリング," 電子情報通信学会論文誌 (D-II), Vol.J92-DII, No.6, pp. 767-776, 2009.

  25. Fujino, A., Ueda, N. and Saito, K., "Semisupervised learning for a hybrid generative/discriminative classifier based on the maximum entropy principle," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol.30, No.3, pp.424-437, 2008, [IEEE Xplore] [DOI link] [IEEE Copyright Notice].

  26. 桑田修平, 山田武士, 上田修功, "ディリクレ過程混合モデルに基づく離散データの共クラスタリング, "情報処理学会論文誌:数理モデル化と応用, pp. 60-73, 2008, [情報処理学会].

  27. 岩田具治, 山田武士, 上田修功, "購買順序を効率的に用いた協調フィルタリング," 情報処理学会論文誌:数理モデル化と応用 Vol.49, No.SIG4 (TOM20), pp. 125-134, 2008.

  28. Naud, A., Usui, S., Ueda, N. and Taniguchi T., "Visualization of documents and concepts in Neuroinformatics with the 3D-SE Viewer," Neuroinformatics, 2007.

  29. 桑田修平, 上田修功, "一括予測型協調フィルタリング," 情報処理学会論文誌, Vol.48, No.SIG 15(TOM 18), pp. 153-162, 2007, [情報処理学会].

  30. 川前徳章, 山田武士, 上田修功, "Relative Innovator の発見によるパーソナライズ手法の提案," FIT2007 Letters, Vol.6, pp.99-102, 2007.

  31. Kuwata, S. and Ueda, N., "An efficient collaborative filtering algorithm based on marginal rating distributions," International Journal of IT & IC, IEEE CIS, Vol.2, No.1, 2007.

  32. 藤野昭典, 上田修功, 斉藤和巳, "複数の構成要素データを扱う多クラス 分類器の半教師有り学習法," 情報処理学会論文誌, 数理モデル化と応用(TOM), 48(SIG 15), pp. 163-175, 2007, [情報処理学会].

  33. Fujino, A., Ueda, N. and Saito, K., "A hybrid generative/discriminative approach to text classification with additional information," Information Processing & Management, Elisevier, Vol.43, No.2, pp. 379-392, 2007.

  34. Usui, S., Plames, P., Nagata, K., Taniguchi, T. and Ueda, N., "Keyword extraction, ranking, and organization for the neuroinfomatics platform," Biosystems, Elsevier Science, Vol.88, Issue 3, pp. 334-342, 2007, [Biosystems].

  35. Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. and Tenenbaum, J., "Parametric Embedding for Class Visualization," Neural Computation Vol. 19, No. 9, pp. 2536-2556, 2007.

  36. Kawamae, N., Yamada, T. and Ueda, N.,"Personalized Ranking by Identifying, RelativeInnovators," FIT2007 L, Vol.6, pp.99-102, 2007.

  37. 上田修功, 山田武士, "ノンパラメトリックベイズモデル," 応用数理 Vol.17, No.3, pp.196-214, 2007.

  38. 藤野昭典, 上田修功, 斉藤和巳, "最大エントロピー原理に基づく付加情報 の効率的な利用によるテキスト分類," 情報処理学会論文誌, Vol.47, No.10, pp. 2929-2937, 2006, [情報処理学会].

  39. 藤野昭典, 上田修功, 斉藤和巳, "半教師あり学習のための生成・識別ハイブリッド分類器の設計法人工知能学会論文誌, Vol.21, No.3, pp. 301-309, 2006.

  40. Ueda, N. and Saito, K., "Parametric mixture models for multi-topic text," Systems and Computers in Japan, Vol.37, No.2, pp. 56-66, 2006, [Systems and Computers in Japan].

  41. 上田 修功, "アンサンブル学習," 情報処理学会論文誌(招待論文), Vol.46, No.SIG15(CVIM 12), pp. 11-20, 2005, [情報処理学会 ].

  42. 岩田具治, 斎藤和巳, 上田修功, "パラメトリック埋め込み法によるクラス構造の可視化," 情報処理学会論文誌, vol.46,no.9, pp. 2337-2346, 2005.

  43. 藤野 昭典, 上田 修功,  斉藤 和巳, "生成・識別ハイブリッドモデルに基づく半教師あり学習," FIT2005 情報科学技術レターズ, 2005.

  44. Kimura, M., Saito K. and Ueda, N., "Modeling network growth with directional attachment and communities," Systems and Computers in Japan, Vol. 35, No. 8, pp. 1-11, 2004, [Systems and Computers in Japan].

  45. 岩田具治, 斉藤和巳, 上田修功, "事後確率構造の可視化," 情報科学技術レターズ, Vol. 3, pp. 119-120, 2004.

  46. 金田 有二, 斉藤 和巳, 上田 修功, "文書分類体系間の対応関係の自動抽出," FIT2004 情報科学技術レターズ, Vol. 3, pp.121-122, 2004.

  47. 金田 有二, 上田 修功, "高次元データに対して頑健な文書クラスタリング手法," FIT2004 情報科学技術レターズ, Vol. 3, pp. 123-124, 2004.

  48. 藤野昭典, 上田修功, 斉藤和巳, "交差確認法に基づく適合性フィードバック," FIT2004 情報科学技術レターズ, Vol. 3, pp. 53-54, 2004.

  49. Kimura, M., Saito, K. and Ueda, N., "Modeling share dynamics by extracting competition structure," Physica D, Vol.198, pp. 51-73, 2004.

  50. Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N., "Variational Bayesian Estimation and Clustering for Speech Recognition," IEEE transaction on Speech and Audio Processing, Vol. 12, pp.365-381, 2004.

  51. Kimura, M., Saito, K. and Ueda, N., "Modeling of growing networks with directional attachment and communities," Neural Networks, Vol. 17, No. 7, pp. 975--988, 2004.

  52. 上田修功, 斉藤和己, "多重トピックテキストの確率モデル --パラメトリック混合モデル--," 電子情報通信学会論文誌 (D-II), Vol. J87-DII, No.3, pp. 872-883, 2004.

  53. Ueda, N. and Inoue, M., "Extended Tied-Mixture HMMs for Both Labeled and Unlabeled Time Series Data," Journal of VLSI Signal Processing Systems, Vol. 37, pp. 189-197, 2004.

  54. 木村昌弘, 斉藤和巳, 上田修功, "指向性アタッチメントとコミュニティをもつ成長ネットワークモデル," 電子情報通信学会論文誌, Vol. J86-DII, No, 10, pp. 1468-1479, 2003.

  55. 上田修功, 斉藤和己, "類似テキスト検索のための多重トピックテキストモデル" 情報処理学会論文誌, Vol. 44, No. SIG14(TOM9), pp. 1-8, 2003, [情報処理学会].

  56. 山田武士, 斉藤和巳, 上田修功, "クロスエントロピー最小化に基づくネットワークデータの埋め込み," 情報処理学会論文誌, Vol. 44, No. 9, pp. 2401-2408, 2003, [情報処理学会]

  57. Inoue, M. and Ueda, N., "Exploitation of unlabeled sequences in hidden markov models," IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI), Vol. 25, No. 12, pp1570-1581, 2003.

  58. 渡部晋治, 南泰浩, 中村篤, 上田修功, "ベイズ的基準を用いた 状態共有型HMM構造の選択" 電子情報通信学会論文誌, (D-II), Vol. J86-DII, No. 6, pp. 776-786, 2003.

  59. 井上雅史, 上田修功, "隠れマルコフモデルにおけるクラスラベル無しデータの利用 " 電子情報通信学会論文誌, (D-II), Vol. J86-DII, No.2, pp. 173-183, 2003.

  60. Ueda, N. and Ghahramani, Z., "Bayesian model search for mixture models based on optimizing variational bounds," Neural Networks, Vol. 15, No.10, pp. 1223-1241, 2002.

  61. 上田修功,"最良モデル探索の変分ベイズ法," 人工知能学会論文誌, Vol.16, No.2, SP-F, 2001.

  62. 鈴木 敏, 上田修功, "混合回帰モデルのためのSMEMアルゴリズム" 電子情報通信学会論文誌, (D-II), Vol. J83-DII, No. 12, pp. 2777-2785, 2000.

  63. Ueda, N., "EM algorithm with split and merge operations for mixture models (invited)," Transactions of IEICE, Vol. E83-D, No. 12, pp. 2047-2055, 2000.

  64. 鈴木 敏, 上田修功, "モジュール競合学習を用いた適応的クラスタリング" 電子情報通信学会論文誌, (D-II), Vol. J83-DII, No. 6, pp. 1529-1538, 2000.

  65. Ueda, N., Nakano, R., Ghahramani, Z. and Hinton, G. E., "SMEM algorithm for mixture models," Neural Computation, Vol. 12, No. 9, pp. 2109-2128, 2000.

  66. Ueda, N., Nakano, R., Ghahramani, Z. and Hinton, G. E., "Split and merge EM algorithm for improving Gaussian mixture density estimates (invited), "Journal of VLSI Signal Processing, Vol. 26, pp.133-140Z, 2000.

  67. Ueda, N., "Optimal linear combination of neural networks for improving classification performance," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 22, No. 2, pp. 207-215, 2000.

  68. 上田修功, 中野良平, "確率的混合部分空間法" 電子情報通信学会論文誌, (D-II), Vol. J82-DII, No. 12, pp. 2394-2401, 1999.

  69. 上田修功, 中野良平, "混合モデルのための併合分割操作付きEMアルゴリズム" 電子情報通信学会論文誌(D-II), Vol. J82-DII, No. 5, pp. 930-940, 1999.

  70. 上田修功, "分類誤り最小基準に基づくニューラルネット識別機の最適線形統合法,"電子情報通信学会論文誌(D-II), Vol. J82_DII, No. 3, pp. 522-530, 1999.

  71. Ueda, N. and Nakano, R., "Deterministic annealing EM algorithm," Neural Networks, Vol.11, No. 2, pp.271-282, 1998.

  72. 上田修功, 中野良平, "アンサンブル学習の汎化誤差解析," 電子情報通信学会論文誌(D-II), Vol. J80-DII, No. 9, pp. 2512-2521, 1997.

  73. 上田修功, 中野良平, "確定的アニーリングEMアルゴリズム," 電子情報通信学会論文誌(D-II), Vol.J80-DII, No.1, pp. 267-276, 1997.

  74. Ueda, N. and Mase, K., "Tracking moving contours using energy-minimizing elastic contour models," International Journal of Pattern Recognition and Artificial Intelligence, Vol. 9, No. 3, pp. 465-484, 1995.

  75. Ueda, N. and Nakano, R., "A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers," Neural Networks, Vol.7, No.8, pp. 1211-1227, 1994.

  76. 上田修功, 中野良平, "ベクトル量子化器設計のための淘汰型競合学習法 -等ひずみ原理とその実現アルゴリズム-" 電子情報通信学会論文誌(D-II), Vol. J77-DII, No. 11, pp. 2265-2278, 1994.

  77. Ueda, N. and Suzuki, S., "Learning visual models from shape contours using multiscale convex/concave structure matching," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 15, No. 4, pp. 337-352, 1993, [IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)].

  78. Suzuki, S., Ueda, N. and Sklansky, J., "Graph-Based Thinning for Binary Images," International Journal of Pattern Recognition and Artificial Intelligence, Vol. 7, No. 5 pp. 1009-1030, 1993.

  79. 上田修功, 間瀬健二, 末永康仁, "弾性輪郭モデルとエネルギー最小化原理に基づく動輪郭追跡手法," 電子情報通信学会論文誌(D-II), Vol. J75-DII, No. 1, pp. 111-120, 1992.

  80. 上田修功, 鈴木智, "凹凸構造の一般化に基づく輪郭形状モデルの自動獲得," 電子情報通信学会論文誌(D-II), Vol. J74-DII, No. 2, pp. 220-229, 1991.

  81. 上田修功, 鈴木智, "多重スケールの凹凸構造を用いた変形図形のマッチングアルゴリズム," 電子情報通信論文誌(D-II),  Vol. J73-DII, No. 7, pp. 992-1000, 1990.

  82. 上田修功, 名倉正計, 小杉 信, 森 克己, "図面の2値化のための画質改善," テレビジョン学会誌, Vol. 42, No. 8, pp. 831-836, 1988.

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国際会議論文
  1. Fujiwara, Y., Marumo, N., Blondel, M., Takeuchi, K., Kim, H., Iwata, T. and Ueda, N., "Scaling Locally Linear Embedding," In Proc. SIGMOD 2017, pp. 1479-1492, 2017.

  2. Kim, H., Iwata, T., Fujiwara, Y. and Ueda, N., "Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes," In Proc. AAAI 2017, pp. 132-139, 2017.

  3. Ichimura1, T., Fujita1, K., Yamaguchi, T., Hori1, M., Lalith1, M. and Ueda, N., "AI with Super-computed Data for Monte Carlo Earthquake Hazard Classification," Proc. of the international conference for high performance computing, networking, strage and analysis (SC2017),  2017.(to appear)

  4. Kimura, A., Takahashi, I., Tanaka, M., Yasuda, N., Ueda, N. and Yoshida, N., "Single-epoch supernova classification with deep convolutional neural networks," The 1st US-Japan Workshop 2017, 2017.(to appear)

  5. Toda, T., Inoue, S. and Ueda, N., "Mobile activity recognition through training labelswith inaccurate activity segments," 13th Annucal International Conference on Mobile and Ubiquitous Systems 2016.(MobiQuious2016), 2016.

  6. Blondel, M., Ishihata, M., Fujino, A. and Ueda, N., "Higher-order factorization machines," Advances in Neural Information Processing Systems (NIPS2016), 2016.

  7. Fujino, A. and Ueda, N., "A semi-supervised AUC optimization method with generative models," IEEE International Conference on Data Mining (ICDM2016), 2016.

  8. Takeuchi, K. and Ueda, N., "Graph regularized non-negative tensor completion for spatio-temporal data analysis," The Second International Workshop on Smart Cities, 2016.

  9. M. Blondel, Fujino, A. and Ueda, N., "Efficient training of low-rank polynomial models," International Conference on Machine Learning (ICML2016), 2016. (to appear).

  10. Ishiguro, K., Sato, I., Ueda, N., Nakano, M. and Kimura, S., "Infinite plaid models for infinite bi-clustering," Proc. the 27th AAAI Conference on Artificial Intelligence (AAAI2016).

  11. Ueda, N., Naya, F., Shimizu, H., Iwata, T., Okawa, M. and Sawada, H., "Real-time and proactive navigation via spatio-temporal prediction, "Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers(UbiComp 2015), pp. 1559-1566, 2015.

  12. Inoue, S., Ueda., N., Nohara, Y. and Nakashima, N., "Mobile activity recognition for a whole day: recognizing real nursing activities with big dataset," Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing(UbiComp2015), pp. 1269-1280, 2015.

  13. Baba, Y., Kashima, H., Nohara, Y., Kai, E., Ghosh, P., Islam, R., Ahmed, A., Kuroda, M., Inoue, S., Hiramatsu, T., Kimura, M., Shimizu, S., Kobayashi, K., Tsuda, K., Sugiyama, M., Blondel, M., Ueda, N., Kitsuregawa, M. and Nakashima, N., "Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries," Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2015), pp. 1681-1690, 2015.

  14. Blondel, M., Fujino, A. and Ueda, N., "Convex Factorization Machines," Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML PKDD), Part II, LNAI 9285, pp. 19-35, 2015.

  15. Matsubara, Y., Sakurai, Y., Ueda, N. and Yoshikawa M., "Fast and Exact Monitoring of Co-Evolving Data Streams," 2014 IEEE International Conference on Data Mining(ICDM), pp. 390-399, 2014.

  16. Blondel, M. Fujino, A. and Ueda, N., "Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex," 22nd International Conference on Pattern Recognition (ICPR2014), pp. 1289-1294, 2014

  17. Nakano, M., Ishiguro, K., Kimura, A., Yamada, T. and Ueda, N., "Rectangular tiling process," Proceedings of The 31st International Conference on Machine Learnin (ICML2014), pp. 361–369, 2014.

  18. Blondel, M., Kubota, Y. and Ueda, N., "Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion," Proc. 17th International Conference on Artificial Intelligence and Statistics (AISTATS2014), Vol.33, pp. 96-104, 2014.

  19. Ueda, N., Tanaka, Y. and Fujino, A., "Robust Naive Bayes Combination of Multiple Classifications," The Impact of Applications on Mathematics, Proceedings of the Forum of Mathematics for Industry 2013, Springer, pp. 141-156, 2014.

  20. Iwata, T., Hirao, T. and Ueda, N., "Unsupervised Cluster Matching via Probabilistic Latent Variable Models," Proc. the 24th AAAI Conference on Artificial Intelligence (AAAI2013), pp. 445-451, 2013.

  21. Ishiguro, K., Ueda, N. and Sawada, H., "Subset Infinite Relational Models," Proc. International Conference on Artificial Intelligence and Statistics (AISTATS 2012), Society for AI and Statistics, Vo. 22, pp. 547-555, 2012.

  22. Sawada, H., Kameoka, H., Araki, S. and Ueda, N., "Efficient Algorithms for Multichannel Extensions of Itakura-Saito Nonnegative Matrix Factorization," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), pp. 261-264, 2012.

  23. Sun, X., Kashima, H., Tomioka, R. and Ueda, N., "Large Scale Real-life Action Recognition Using Conditional Random Fields with Stochastic Training," 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2011), Part II, LNAI 6635, pp. 222–233, 2011.

  24. Sun, X., Kashima, H., Tomioka, R., Ueda, N. and Li, P., "A New Multi-Task Learning Method for Personalized Activity Recognition," 11th IEEE International Conference on Data Mining (ICDM 2011), pp. 1218-1223, 2011.

  25. Sawada, H., Kameoka, H., Araki, S. and Ueda, N., "New Formulations and Efficient Algorithms for Multichannel NMF," 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2011), pp. 153-156, 2011.

  26. Aoyama, K., Sawada, H., Ueda, N. and Saito, K., "Fast approximate similarity seach based in degree-reduced neighborhood graphs," Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2011), pp. 1055-1063, 2011.

  27. Sawada, H., Kameoka, H., Araki, S. and Ueda, N., "Formulations and Algorithms for Multichannel Complex NMF," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011) , pp. 229-232, May, 2011.

  28. Iwata, T., Yamada, T., Sakurai, Y. and Ueda, N., "Online Multiscale Dynamic Topic Models," Proc. the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2010), pp. 663-672, 2010.

  29. Sun, X., Kashima, H., Matsuzaki, T. and Ueda, N., "A Robust, Accurate, and Fast Stochastic Gradient Training Method for Modeling Latent-Information in Data," IEEE International Conference on Data Mining (ICDM2010), pp.1067-1072, Sydney, Australia, 2010.

  30. Hachiya, H., Sugiyama, M. and Ueda, N., "Coping with new user problems: Transfer learning in accelerometer-based human activity recognition," NIPS 2010 Workshop on Transfer Learning by Learning Rich Generative Models, 2010.

  31. Fujino, A., Ueda, N. and Nagata, M., "A Robust Semi-supervised Classification Method for Transfer Learning," Proc. of the 19th ACM international conference on Information and knowledge management (CIKM 2010), pp. 379-388, 2010.

  32. Ishiguro, K., Iwata, T., Ueda, N. and Tenenbaum, J. B., "Dynamic Infinite Relational Model for Time-varying Relational Data Analysis," Advances in Neural Information Processing Systems 23 (NIPS2010), 2010.

  33. Aoyama, K., Watanabe, S., Sawada, H., Minami, Y., Ueda, N. and Saito, K.,"Fast Similarity Search On A Large Speech Data Set With Neighborhood Graph Indexing," International Conference on Acoustics, Speech, and Signal Processing(ICASSP2010), pp. 5358-5361, 2010.

  34. Usui, S., Kamiji, N. L., Taniguchi, T. and Ueda N., "RAST: A Related Abstract Search Tool," International Conference on Neural Information Processing (ICONIP2009), Part II, LNCS 5864, pp. 189–195, 2009.

  35. Iwata, T., Yamada, T. and Ueda, N., "Modeling Social Annotation Data with Content Relevance using a Topic Model," Advances in Neural Information Processing Systems (NIPS2009), pp. 835-843, 2009.

  36. Iwata, T., Watanabe, S., Yamada, T. and Ueda, N., "Topic Tracking Model for Analyzing Consumer Purchase Behavior," Proc. of 21st International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 1427-1432, 2009.

  37. Iwata, T., Yamada, T. and Ueda, N., "Probabilistic Latent Semantic Visualization: Topic Model for Visualizing Documents," Proc. of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Minig (KDD2008), pp.363-371, 2008.

  38. Ishiguro, K., Yamada, T. and Ueda, N., "Simultaneous Clustering and Tracking Unknown Number of Objects," Proc. of the 19th IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR08), pp. 1-8, 2008, [CVPR08]

  39. Usui, S., Naud, A., Ueda, N. and Taniguchi, T., "3D-SE Viewer: A Text Mining Tool based on Bipartite Graph Visualization," 20th International Joint Conference on Neural Networks (IJCNN'07), 2007.

  40. Kuwata, S. and Ueda, N., "One-shot Collaborative Filtering," IEEE CIDM2007, Vol.1, No.1, pp.300-307, 2007.

  41. Fujino, A., Ueda, N. and Saito, K., "Semi-supervized learning for multi-component data classification,"Proc. of International Joint Conference on Areificial Intelligence (IJCAI2007), pp. 2754-2759, 2007.

  42. Kuwata, S. and Ueda N., "One-shot collaborative filtering," Proc. of IEEE Symposium on Compututational Intelligence and Data Mining (CIDM2007), pp. 300-307, 2007.

  43. Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T. and Ueda, N., "Learning systems of concepts with an infinite relational model," Proc. of the 21st National Conference on Artificial Intelligence(AAAI-06), pp. 381-388, 2006.

  44. Iwata, T., Saito, K. and Ueda, N., "Visual nonlinear discriminant analysis for classifier design," Proc. of the 14th European Symposium on Artificial Neural Networks (ESANN2006), pp.283-288, 2006.

  45. Usui, S., Palmes P., Nagata, K., Taniguchi, T. and Ueda, N., "Extracting Keywords from Research Abstracts for the Neuroinformatics Platform Index Tree," Proc. of International Joint Conference on Neural Networks (IJCNN2006), pp. 5045-5050, 2006.

  46. Ueda, N., "Bayesian probabilistic models for data partitioning and their applications," Proc. of the 17th International Symposium on Mathematical Theory of Networks and Systems (MTNS2006), MoP08.3, pp. 364-369, 2006.

  47. Saito, K. and Ueda, N., "Filtering Search Engine Spam based on Anomaly Detection Approach," Proc. of the KDD2005 Workshop on Data Mining Methods for Anomaly Detection, pp.62-67, 2005.

  48. Usui, S., Palmes, P., Nagata, K., Taniguchi, T. and Ueda, N., "Relevance keyword extraction, ranking, and organization for the neuroinformatics platform, Proc. of International Conference on Biological Computation," Proc. of BIOCOMP, 2005.

  49. Fujino, A., Ueda, N. and Saito, K., "A Classifier design based on combining multiple components by maximum entropy principle," Proc. of the 2nd Asia Information Retrieval Symposium (AIRS2005), LNCS 3689, pp. 423–438, 2005.

  50. Kimura, M., Saito, K. and Ueda, N., "Multinomial PCA for extracting major latent topics from document streams," Proc. International Joint Conference on Neural Networks ( IJCNN2005), Vol. 1, pp.238-243, 2005.

  51. Fujino, A., Ueda, N. and Saito, K., "A hybrid generative/discriminative approach to semi-supervised classifier design," Proc. of the 20th National Conference on Artificial Intelligence (AAAI-05), pp. 764-769, 2005.

  52. Inoue, M. and Ueda, N., "Retrieving lightly annotated images using image similarities," ACM Symposium on Applied Computing (SAC), Special Track on Information Access and Retrieval (IAR)Santa Fe, March 14-17, pp.1031-1037, 2005.

  53. Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. L. and Tenenbaum, J. B., "Parametric Embedding for Class Visualization, " Advances in Neural Information Processing Systems 17 (NIPS2004), pp. 617-624, 2005.

  54. Kimura, M., K. Saito and N. Ueda, "Modeling share dynamics by extracting competition structure," Proc. of the 5th International Conference on Complex Systems, p. 72, 2004.

  55. Kaneda, Y., Ueda, N. and Saito, K., "Extended Parametric Mixture Model for Robust Multi-labeled Text Categorization," Proc. of the 8-th International Conference on Knowledge-Based Intelligent Information & Engineering Systems, Vol. 3214 of Lecture Notes in Computer Science, pp. 616-623, 2004.

  56. Ueda, N. and Saito, K., "Simplex mixture models for multi-topic text," In Science of Modeling, ISM Report on Research and Education No.17, The Institute of Statisitical Mathematics, pp. 380-381, 2003.

  57. Kimura, M., Saito, K. and Ueda, N., "Modeling share dynamics by extracting competition structure," In Science of Modeling, ISM Report on Research and Education No.17, The Institute of Statisitical Mathematics, pp.366-367, 2003.

  58. Yamada, T., Saito, K. and Ueda, N., "Cross-entropy based embedding for relational data," International Conference on Machine Learning (ICML2003), pp. 832-839, 2003.

  59. Watanabe, S., Minami, Y., Nakamura, A. and N. Ueda, "Bayesian acoustic modeling for spontaneous speech recognition," IEEE Workshop on Spontaneous Speech Processing and Recognition (SSPR03), pp. 47-50, 2003.

  60. Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N., "Application of variational bayesian estimation and clustering to acoustic model adaptation," IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP03), Vol. 1, pp. 568-571, 2003.

  61. Kimura, M., Saito, K. and Ueda, N., "Modeling of growing networks with directional attachment and communities," European Symposium on Artificial Neural Networks (ESANN03), pp. 15-20, 2003.

  62. Kimura, M., Saito, K. and Ueda, N., "Modeling of growing networks with communities," IEEE International Workshop on Neural Networks for Signal Processing (NNSP2002), pp. 189-198, 2002.

  63. Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N., "Application of Variational Bayesian Approach to Speech Recognition," Proc. of Advances in Neural Information Processing Systems 15 (NIPS15), MIT Press, pp. 1261-1268, 2002.

  64. Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N., "Constructing shared-state HMMs based on a Bayesian approach," International Conference on Spoken Language Processing (ICSLP02), Vol. 4, pp. 2669-2672, 2002.

  65. Ueda, N. and Saito, K., "Parametric mixture models for multi-topic text," Neural Information Processing Systems 15(NIPS15), MIT Press, pp. 737-744, 2002.

  66. Ueda, N. and Saito, K., "Singleshot detection of multi-category text using parametric mixture models," ACM SIG Knowledge Discovery and Data Mining (SIGKDD2002), pp. 626-631, 2002.

  67. Inoue, M. and Ueda, N., "HMMs for both labeled and unlabed time series data," IEEE Neural Networks for Signal Processing (NNSP2001), pp. 93-102, 2001.

  68. Ueda, N. and Ghahramani, Z., "Optimal model inference for Baysian mixture of experts," IEEE Neural Networks for Signal Processing (NNSP2000), pp. 145-154, 2000.

  69. Ueda, N., Nakano, R., Ghahramani, Z. and Hinton, G. E., "Pattern classification using a mixture of factor analyzers," IEEE Neural Networks for Signal Processing (NNSP99), pp. 525-534, 1999.

  70. Ueda, N., Nakano, R., Ghahramani, Z. and Hinton, G. E., "SMEM algorithm for mixture models," Neural Information Processing Systems 11 (NIPS11), pp. 599-605, 1999.

  71. Ueda, N., Nakano, R., Ghahramani, Z. and Hinton, G. E., "Split and merge EM algorithm for improving Gaussian mixture density estimates," IEEE Neural Networks for Signal Processing (NNSP98), pp. 274-283, 1998.

  72. Ueda, N. and Nakano, R., "Combining discriminant-based classifiers using the minimum classification error discrimininant,"IEEE Neural Networks for Signal Processing (NNSP97), pp. 365-374, 1997.

  73. Suzuki, S. and Ueda, N., "Self-organization of feature columns and its application to object classification," Proceedings of International Conference on Neural Information Processing (ICONIP97), pp. 1166-1169, 1997.

  74. Ueda, N. and Nakano, R, "Generalization error of ensemble estimators," Proceedings of International Conference on Neural Networks (ICNN96), pp. 90-95, 1996.

  75. Ueda, N. and Nakano, R, "Deterministic annealing variant of the EM algorithm," Neural Information Processing Systems 7 (NIPS7), MIT Press, Cambridge MA, pp. 545-552, 1995.

  76. Ueda, N. and Nakano, R, "A new maximum likelihood training and application to probabilistic neural networks," Proceedings of International Conference on Artificial Neural Networks (ICANN95), pp. 497-504, 1995.

  77. Ueda, N. and Nakano, R, "Estimating expected error rates of neural network classifiers in small sample size situations: A comparison of cross-validation and bootstrap," International Conference on Neural Networks (ICNN95), pp. 101-104, 1995.

  78. Ueda, N. and Nakano, R, "Mixture density estimation via EM algorithm with deterministic annealing," Proceedings of IEEE Neural Networks for Signal Processing (NNSP94), pp. 69-77, 1994.

  79. Ueda, N. and Nakano, R, "A new learning approach based on equidistortion principle for optimal vector quantizer Design," Proceedings of IEEE Neural Networks for Signal Processing (NNSP93), pp. 362-371, 1993.

  80. Ueda, N. and Nakano, R, "Competitive and selective learning method for designing optimal vector quantizers," Proceedings of IEEE International Conference on Neural Networks (ICNN93), pp. 1444-1450, 1993.

  81. Ueda, N. and Mase, K., "Tracking moving contours using energy-minimizing elastic contour models,"Proceedings of European Conference on Computer Vision (ECCV92), pp. 453-457, 1992.

  82. Suzuki, S. and Ueda, N., "Robust vectorization using graph-based thnning and reliability-based line approximation," Proceedings of IEEE Conference on Computer Vision (CVPR91), pp. 494-500, 1991.

  83. Ueda, N. and Suzuki, S., "Automatic shape model acquisition using multiscale segment matching," Proceedings of International Conference on Pattern Recognition (ICPR90), pp. 897-902, 1990.

  84. Ogawa, H. Kawada, E. and Ueda, N., "Application of image processing equipment with multiprocessors to line-drawing recognition," Proceedings of SPIE-845, pp. 97-103, 1987.

  85. Okudaira, M. Ueda, N. and Aoki, U., "Image enhancement of handwritten drawings and their recognition followed by interactive processing," Proceedings of SPIE-707, pp. 42-50, 1986.

招待解説論文・記事
  1. Ueda, N, Tanaka, Y. and Fujino,A., "Bayesian Meta-Learning and its Application to High-Level Real Nursing Activity Recognition Using Accelerometers" Springer, 2014.(to appear)

  2. 上田修功, "ベイズモデルに基づく関係データ解析技術," 電子情報通信学会誌, H26年5月号特集:データを読み解く技術──ビッグデータ, e-サイエンス, 潜在的ダイナミクス──

  3. Ueda, N, "Cyber Physical Systems, Creating New Value from Sensor Network Information, NII Today, No.45, 2013.

  4. Fujino, A., Ueda, N. and Nagata, M., "Adaptive semi-supervised learning on labeled and unlabeled data with different distributions," Knowledge and Information Systems(KAIS), Vol. 37, Issue 1, pp. 129-154, Springer, 2013, (invited paper).

  5. 上田修功, "汎用学習手法 EMアルゴリズム," 計測自動制御学会誌, Vol. 44, No. 5, pp. 333-338, 2005.

  6. 上田修功, "Webサイエンス研究が目指すもの," 技術情報誌OHM, .Vol. 91, No. 10, pp. 6-7, 2004.

  7. 上田修功, "ベイズ学習のアルゴリズム -高次元積分の近似手法-," 人工知能学会誌, 特集(統計モデルと学習の数理), Vol. 19, No. 6, 2004.

  8. 上田修功, 斉藤和巳, "多重トピックテキストの確率モデル,"(全2回) 情報処理学会誌, Vol. 45, No. 2, 3, 2004.

  9. 上田修功, "確率モデルと統計的学習," Computer Today, No.114, 2003.

  10. 上田修功, "ベイズ学習,"(全4回)電子情報通信学会誌, Vol. 85, No. 4,6,7,8, 2002.

  11. 上田修功, "アンサンブル学習," 計測自動制御学会誌, Vol. 41, pp.248, 2002.

  12. 上田修功, "ベイズ学習法の 最前線 -変分ベイズ法-," 情報処理学会誌, Vol. 42, No. 1, 2001.

  13. 上田修功, "統計的学習研究探訪," 情報処理学会誌, Vol. 41, No. 6, pp. 730-733, 2000.

  14. 上田修功, 中野良平, "確定的アニーリングEMアルゴリズム," 計測と制御, Vol. 38, No. 7, pp. 444-449, 1999.

  15. 上田修功, 中野良平, "確定的アニーリング -もうひとつのアニーリング-," 人工知能学会誌, Vol. 12, No. 5, pp. 689-697, 1997.

  16. 間瀬 茂, 上田修功, "モルフォロジーと画像解析I,"電子情報通信学会誌, Vol. 64, No. 2, pp. 166-174, 1991.

  17. 上田修功, 間瀬 茂, "モルフォロジーと画像解析 II," 電子情報通信学会誌, Vol. 64, No. 3, pp. 271-279, 1991


NTT機関論文誌および研究実用化報告

  1. 上田修功, "挑戦する研究者たち," NTT技術ジャーナル インタビュー記事, Vol.25 No.9, pp. 40-43, 2013.

  2. 上田修功, "ビッグデータを活かす機械学習技術," NTT技術ジャーナル 特集 NTT R&Dフォーラム2013 ワークショップ, Vol.25, No.4, pp 31-35, 2013.

  3. Ueda, N., "Communication Science for the Big Data Era," NTT Technical Review, Vol. 10, No. 11, 2012.

  4. 藤野昭典, 上田修功, 斎藤和巳, "テキスト自動分類のための半教師あり学習技術," NTT技術ジャーナル, Vol. 19, No. 6, pp. 26-28, 2007.

  5. Ueda, N., "Web Science Research," NTT Technical Review}, Vol. 3, No. 3, pp. 12-14, 2005.

  6. 上田修功, "Webサイエンス研究," NTT技術ジャーナル, Vol. 16, No. 6, pp. 22, 2004.

  7. 上田修功, 中野良平, "最適ベクトル量子化を実現する淘汰型競 合学習法,"NTT R&D, Vol.42. No.6, 1993.

  8. 中野良平, 上田修功, 斎藤和巳, 山田武士, "学習機構の 研究,"NTT R&D, Vol. 42, No. 9, pp. 1175-1184, 1993.

  9. 上田修功, 間瀬健二, 末永康仁, "エネルギー最小化弾性モデル による動輪郭追跡法," NTT R&D, Vol. 42, No. 4, pp. 477-486, 1993.

  10. 上田修功, 鈴木智, "多重スケール凹凸構造マッチ ング, " NTT R&D, Vol. 40, No. 3, pp.399-406, 1991.

  11. 河田悦生, 上田修功, 小川 博, 小杉 信, 手書き図面の図形認識法," NTT研究実用化報告第37巻, 第3号, pp. 217-223, 1988.

  12. 上田修功, 名倉正計, 星野肇夫, 森 克己, "手書き図面の画質改善法," 研究実用化報告第37巻, 第3号, pp. 211-216, (1988).

登録特許
  1. 特願2008-109409, 特許第5139874号, "ラベル付与装置, ラベル付与プログラム, ラベル付与プログラムが記録された記録媒体, および, ラベル付与方法."

  2. 特願2008-060339, 特許第4972016号, "動画像処理方法, 動画像処理装置および動画像処理プログラム."

  3. 特願2008-002218, 特許第4934058号, "共クラスタリング装置, 共クラスタリング方法, 共クラスタリングプログラム, および, そのプログラムを記録した記録媒体."

  4. 特願2005-161362, 特許第4490876号, "コンテンツ分類方法, コンテンツ分類装置, コンテンツ分類プログラムおよびコンテンツ分類プログラムを記録した記録媒体."

  5. 特願2004-296475, 特許第4460417号, "自動分類方法, 自動分類プログラム, 記録媒体, および, 自動分類装置."

  6. 特願2003-115148, 特許第4080939号, "ネットワークデータ低次元埋込方法, ネットワークデータ低次元埋込装置, ネットワークデータ低次元埋込プログラム及びそのプログラムを記録した記録媒体."

  7. 特願2002-277225, 特許第3920749号, "音声認識用音響モデル作成方法, その装置, そのプログラムおよびその記録媒体, 上記音響モデルを用いる音声認識装置."

  8. 特願2002-204434, 特許第3868344号, "テキストの多重トピックス抽出方法および装置, テキストの多重トピックス抽出プログラム, ならびに該プログラムを記録した記録媒体."

  9. 特願2002-113905, 特許第3964722号, "隠れマルコフモデル作成装置, 方法, プログラム, 記録媒体および音声認識装置, 方法, プログラム, 記録媒体."

  10. 特願平05-260908, 特許第3203609号, "ベクトル量子化器の設計方法およびベクトル量子化器."

  11. 特願平03-314759, 特許第2817885号, "ベクトル化方法."

  12. 特願平03-314758, 特許第2694761号, "細線化方法."

  13. 特願平01-125541, 特許第2938887号, "輪郭図形の整合方法."

  14. 特願平01-056182, 特許第1967340号, "閉曲線の凹凸構造整合方法."

  15. 特願昭60-032885, 特許第1946099号, "図面の画質改善処理装置."


著書・訳書
  1. 監修 坂内正夫, "ビッグデータを開拓せよ 解析が生む新しい価値," 角川インターネット講座 第7巻 第1部 第5章(データに語らせる科学)執筆, KADOKAWA, 2015.

  2. 石井健一郎, 上田修功 共著, "続・わかりやすい パターン認識 ―教師なし学習入門―," オーム社, 2014

  3. 上田修功 訳:サイエンスパレットシリーズ "統計学", 丸善出版, 2014
    (原著:"Statistics: VSI," David J. Hand著、"Statistics," Oxford Univ. Press)

  4. 上田修功 "科学事典(第2版), 変分ベイズ法"執筆, 編集代表 広中平祐 丸善株式会社, 2007.

  5. 樺島祥介, 上田修功 共著, "統計科学のフロンティア11, 計算統計I (第III部)," 岩波書店, 2003.

  6. 石井健一郎, 上田修功, 前田英作, 村瀬洋 共著, "わかりやすいパターン認識," オーム社, 1998.

  7. 電子情報通信学会 編, "電子情報通信ハンドブック," 第3.2編(パターン認識理論)一部執筆, 1998.

  8. 甘利, 外山 編, "脳科学ハンドブック," 第9章(学習ベクトル量子化)執筆, 朝倉書店, 1995.

  9. B. K. P. Horn 著, "ROBOT VISION (The MIT Press)," 共訳, 朝倉書店, 1993.

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