English ｜ Home ｜ Our Group | NTT CS Labs.

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
  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
ジャーナル論文
  1. 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(TOM), Vol. 3, No. 1, pp. 1 - 12, 2010.

  2. 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.

  3. 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.

  4. 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.

  5. A. Fujino, N. Ueda, and K. Saito, "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].

  6. 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].

  7. 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.

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

  9. 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].

  10. 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.
  11. S. Kuwata, and N. Ueda, "An efficient collaborative filtering algorithm based on marginal rating distributions," International Journal of IT & IC, IEEE CIS, Vol.2, No.1, 2007.

  12. 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].

  13. A. Fujino, N. Ueda, and K. Saito, "A hybrid generative/discriminative approach to text classification with additional information," Information Processing & Management, Elisevier, Vol.43, pp. 379-392, 2007.

  14. S. Usui, P. Plames, K. Nagata, T. Taniguchi, and N. Ueda, "Keyword extraction, ranking, and organization for the neuroinfomatics platform," Biosystems, Elsevier Science, Vol.88, Issue 3, pp. 334-342, 2007, [Biosystems].
  15. T. Iwata, K. Saito, N. Ueda, S. Stromsten, T. Griffiths, and J. Tenenbaum, "Parametric Embedding for Class Visualization ," Neural Computation Vol. 19, No. 9, pp. 2536-2556, 2007.

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

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

  18. 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].
  19. 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.

  20. N. Ueda, and K. Saito, "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].
  21. 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 ].
  22. 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.

  23. 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.

  24. M. Kimura, K. Saito, and N. Ueda, "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].

  25. 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.

  26. 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.

  27. 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.

  28. 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.

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

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

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

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

  33. 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.

  34. N. Ueda, and M. Inoue, "Extended tied-mixture HMMs for both labeled and unlabeled time series data", to appear Journal of VLSI Signal Processing Systems, Vol. 37, pp. 189-197, 2004.

  35. 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.

  36. 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].

  37. 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]

  38. 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.

  39. 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.

  40. 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.

  41. N. Ueda, and Z. Ghahramani, " Bayesian model search for mixture models based on optimizing variational bounds," Neural Networks, Vol. 15, pp. 1223-1241, 2002.

  42. 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.

  43. 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.

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

  45. 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.

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

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

  48. N. Ueda, "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.

  49. 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.

  50. 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.

  51. 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.

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

  53. 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.

  54. 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.

  55. N. Ueda, and K. Mase, "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.

  56. N. Ueda, and R. Nakano, "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.

  57. 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.

  58. N. Ueda, and S. Suzuki, "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)].

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

  60. 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.

  61. 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.

  62. 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.

  63. 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.

IEEE Copyright Notice

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposed or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

These materials are presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to ashere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

国際会議論文
  1. Shiro Usui, Nilton L. Kamiji, Tatsuki Taniguchi, Naonori Ueda, "RAST: A Related Abstract Search Tool," International Conference on Neural Information Processing (ICONIP) 2009.

  2. Tomoharu Iwata, Takeshi Yamada, Naonori Ueda, "Modeling Social Annotation Data with Content Relevance using a Topic Model, "Advances in Neural Information Processing Systems (NIPS2009), 835-843, 2009

  3. Tomoharu Iwata, Shinji Watanabe, Takeshi Yamada, Naonori Ueda, "Topic Tracking Model for Analyzing Consumer Purchase Behavior, "Proc. of 21st International Joint Conference on Artificial Intelligence (IJCAI-09), 1427-1432, 2009

  4. T. Iwata, T. Yamada, N. Ueda, "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.

  5. K. Ishiguro, T. Yamada, and N. Ueda, "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]

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

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

  8. A. Fujino, N. Ueda, and K. Saito, "Semi-supervized learning for multi-component data classification," to appear in Proc. of International Joint Conference on Areificial Intelligence (IJCAI2007), 2007.

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

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

  11. S. Usui, P. Palmes, K. Nagata, T. Taniguchi and N. Ueda, "Extracting keywords from research abstracts for the neuroinformatics platform index tree," To appear in Proc. of International Joint Conference on Neural Networks (IJCNN2006), 2006.

  12. N. Ueda, "Bayesian probabilistic models for data partitioning and their applications," Proc. of the 17th International Symposium on Mathematical Theory of Networks and Systems (MTNS2006), 2006.

  13. K.. Saito, and N. Ueda, "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.

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

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

  16. M. Kimura, K. Saito, and N. Ueda, "Multinomial PCA for extracting major latent topics from document streams," Proc. of IJCNN, 2005.

  17. A.. Fujino, N. Ueda, and K. Saito, "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.

  18. M. Inoue, and N. Ueda, "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.

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

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

  21. Y. Kaneda, N. Ueda and K. Saito, "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.

  22. N. Ueda, and K. Saito, "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.

  23. M. Kimura, K. Saito, and N. Ueda, "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.

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

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

  26. S. Watanabe, Y. Minami, A. Nakamura, and N. Ueda, "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.

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

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

  29. S. Watanabe, Y. Minami, A. Nakamura, and N. Ueda, "An application of variational Bayesian approach to speech recognition," to appear Advances in Neural Information Processing Systems 15(NIPS15), MIT Press, pp. 1261-1268, 2002.

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

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

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

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

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

  35. N. Ueda, R. Nakano, Z. Ghahramani, and G. E. Hinton, "Pattern classification using a mixture of factor analyzers," IEEE Neural Networks for Signal Processing (NNSP99), pp. 525-533, 1999.

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

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

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

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

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

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

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

  43. N. Ueda, and R. Nakano, "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.

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

  45. N. Ueda, and R. Nakano, "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.

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

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

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

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

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

  51. M. Okudaira, N. Ueda, and U. Aoki, "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
  1. 藤野昭典, 上田修功, 斉藤和巳: 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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. 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).

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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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. 特公平06-068759,　図面の画質改善処理処置.

  2. 特公平06-101034,　閉曲線の凹凸構造整合方法.
    　
  3. 特許第002694761号,　細線化方法.

  4. 特許第002817885号,　ベクトル化方法.

  5. 特許第002938887号,　輪郭図形の整合方法.

  6. 特許第003203609号,　ベクトル量子化器の設計方法.

  7. 特許第003868344号,　テキストの多重トピック抽出方法.

  8. 特許第003920749号,　音声認識用音響モデル作成方法.


著 書
  1. 編集代表　広中 平祐, "数理科学事典（第２版）,"変分ベイズ執筆, 丸善株式会社, 2007.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

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

English ｜ Home ｜ Our Group | NTT CS Labs.
Naonori UEDA