北海道大学大学院情報科学研究科

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2016

Journal Papers

[Konno_2016] Hideaki Konno, Mineichi Kudo, Hideyuki Imai and Masanori Sugimoto, “Whisper to Normal Speech Conversion Using Pitch Estimated from Spectrum.” Speech Communication, 83(2016), 10-20.DOI:10.1016/j.specom.2016.07.001 (intoroduced at New Scientisthttps://www.newscientist.com/article/mg23130882-400-whisper-tech-turns-secrets-into-normal-speech/)

[Koujaku_2016] Sadamori Koujaku, Ichigaku Takigawa, Mineichi Kudo, Hideyuki Imai “Dense core model for cohesive subgraph discovery.” Social Networks, 44(2016), 143-152 DOI:10.1016/j.socnet.2015.06.003

[Kimura_2016a] Keigo Kimura, Mineichi Kudo and Yuzuru Tanaka, “A Column-wise Update Algorithm for Nonnegative Matrix Factorization in Bregman Divergence with Orthogonal Constraint”, Machine Learning: A special issue of selected papers of ACML 2014, 103-2(2016), 285-306.DOI:10.1007/s10994-016-5553-0.

[Lu_2016] Guoliang Lu, Yiqi Zhou, Xueyong Li, Mineichi Kudo. Efficient action recognition via local position offset of 3D skeletal body joints, Multimedia Tools and Applications, 75-6(2016), 3479-3494.DOI:10.1007/s11042-015-2448-1

[Atsu_2016a] Atsuyoshi Nakamura, Ichigaku Takigawa, Hisashi Tosaka, Mineichi Kudo and Hiroshi Mamitsuka, “Mining approximate patterns with frequent locally optimal occurrences”, Discrete Applied Mathematics, 200(2016), 123-152.

Conference Papers

[Suzuki_2016] Shunsuke Suzuki, Mineichi Kudo and Atsuyoshi Nakamura, “Sitting Posture Diagnosis Using a Pressure Sensor Mat.” IEEE International Conference on Identity, Security, and Behavior Analysis ISBA 2016, 1-6.DOI:10.1109/ISBA.2016.7477236

[Sun_2016a] Lu Sun, Mineichi Kudo and Keigo Kimura, “Multi-Label Classification with Meta-Label-Specific Features.” in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico.

[Kimura_2016b] Keigo Kimura, Mineichi Kudo, Lu Sun and Sadamori Koujaku, “Fast Random k-labelsets for Large-Scale Multi-Label Classification.” in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico.

[Suzuuchi_2016] Syota Suzuuchi and Mineichi Kudo, “Location-Associated Indoor Behavior Analysis of Multiple Persons.” in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico.

[Zaya_2016] Batzaya Norov-Erdene, Mineichi Kudo, Lu Sun and Keigo Kimura, “Locality in Multi-Label Classification Problems.” in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico.

[Mine_2016] Mineichi Kudo*, Keigo Kimura, Michael Haindl, Hiroshi Tenmoto, ” Simultaneous Visualization of Samples, Features and Multi-Labels.” in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico.

[Sun_2016b] Lu Sun, Mineichi Kudo and Keigo Kimura, “A Scalable Clustering-Based Local Multi-Label Classification Method.” in Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016), 261-268, 2016, The Hague, Netherlands. DOI:10.3233/978-1-61499-672-9-261

[Kimura_2016c] Keigo Kimura, Mineichi Kudo, Lu Sun, “Simultaneous Nonlinear Label-Instance Embedding for Multi-label Classification.” in Proceedings of the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition (S+SSPR 2016), Merida, Mexico.

[Yamazaki_2016] 山崎 大樹,工藤 峰一,ロバストな顔認識を目標とした多様体構成に関する検討.情報処理北海道シンポジウム2016 , 釧路.

[Suzuuchi_2016] 鈴内 翔太,工藤 峰一,赤外線天井センサによる複数人の行動解析.情報処理北海道シンポジウム2016 , 釧路.

[Suzuuchi_2016] 鈴内 翔太,工藤 峰一,赤外線天井センサによる複数人の行動解析.未来シンポジウム2016 , 小金湯. (最優秀賞)

Technical Reports

[Nakamura_2016] 中村 武憲, 中村 篤祥, 工藤 峰一, ランダム仮説下での出現回数分布を利用した散在反復配列の発見. 第8回データ工学と情報マネジメントに関するフォーラム (DEIM2016). 2016年.

2015

Journal Papers

[Tanaka_15] A. Tanaka, H, Takebayashi, I. Takigawa, H. Imai, and M. Kudo, “Ensemble and Multiple Kernel Regressors: Which Is Better?”, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 98-A(11)(2015), 2315-2324.DOI:10.1587/transfun.E98.A.2315

[Tao_2015] Tao Shuai, Kudo Mineichi, Pei Bingnan, Nonaka Hidetoshi and Toyama Jun, “Multi-person locating and their soft tracking in a binary infrared sensor network”, IEEE Trans. Human-Machine Systems, 45-5(2015), 550-561. DOI:10.1109/THMS.2014.2365466

[Watanabe_2015] Ryo Watanabe, Atsuyoshi Nakamura and Mineichi Kudo, ” An Improved Upper Bound on the Expected Regret of UCB-type Policies for a Matching-Selection Bandit Problem”, Operations Research Letters, 43(2015), 558-563. http://dx.doi.org/10.1016/j.orl.2015.08.008

Conference Papers

[Haindl_2015] Michal Haindl, Stanislav Mike and Mineichi Kudo, “Unsupervised Surface Reflectance Field Multi Segmenter.” Proc. of CAIP2015, Malta, September, 2015, LNCS, 9256,261-273.DOI:10.1007/978-3-319-23192-1_22

[Mikami_2015] Ayako Mikami, Mineichi Kudo and Atsuyoshi Nakamura, “Diversity Measures and Margin Criteria in Multi-class Majority Vote Ensemble.” Proceedings of the 12th International Workshop on Multiple Classifier Systems, 2015, 27-37.

[Koujaku_2015] Sadamori Koujaku, Mineichi Kudo, Ichigaku Takigawa, Hideyuki Imai,“Community Change Detection in Dynamic Networks in Noisy Environment.” Proceedings of the 24th International Conference on World Wide Web Companion , 2015, 793-798. DOI:10.1145/2740908.2742471

[Kimura_2015a] Keigo Kimura and Mineichi Kudo, “Dimension Reduction Using Nonnegative Matrix Tri-Factorization in Multi-label Classification.” Proceedings of The 2015 International Conference on Parallel & Distributed Processing Techniques & Applications: Workshop on Mathematical Modeling and Problem Solving, 2015, 250-255.

[Kimura_2015b] Keigo Kimura and Mineichi Kudo, “Variable Selection for Efficient Nonnegative Tensor Factorization.” In Proceedings of the 2015 IEEE International Conference on Data Mining (ICDM), Nov. 2015, 805 – 810.DOI: 10.1109/ICDM.2015.31

[Tabata_2015] Koji Tabata, Atsuyoshi Nakamura and Mineichi Kudo, “An Algorithm for Influence Maximization in a Two-Terminal Series Parallel Graph and Its Application to a Real Network”, Proceedings of the 18th International Conference on Discovery Science, 2015. (to appear)

[Sun_2015] Lu Sun and Mineichi Kudo, “Polytree-Augmented Classifier Chains for Multi-Label Classification”, In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015, 3834-3840.

Books

[Mine_2015] 大熊 ほか編、「事例で学ぶ数学活用法」、朝倉書店、2015.2. (7.3章工藤担当)

Technical Reports

[Atsu_2015b] 中村 篤祥, 「HedgeとExp3アルゴリズム間の橋渡し」, 信学技報 115(112) , 81-86, 2015.

[Atsu_2015c] David P. Helmbold, Atsuyoshi Nakamura, Manfred K. Warmuth, “Noise Free Multi-Armed Bandit Game”, 人工知能学会研究会資料SIG-FPAI-B501, 16-21, 2015.

2014

Journal Papers

[Endo_2014] Tomomi Endo, Kazuhiro Omura, Mineichi Kudo, “Analysis of Relationship Between Renyi Entropy and Marginal Bayes Error and Its Application to Weighted Native Bayes Classifiers.” International Journal of Pattern Recognition and Artificial Intelligence, 28(2014), 1460006.http://dx.doi.org/10.1142/S0218001414600064

[Ouchi_2014] Koji Ouchi, Atsuyoshi Nakamura, Mineichi Kudo, “An efficient construction and application usefulness of rectangle greedy covers.” Pattern Recognition, 47-3(2014), 1459-1468.http://dx.doi.org/10.1016/j.patcog.2013.09.008

[Hanada_2014] Hiroyuki Hanada, Mineichi Kudo and Atsuyoshi Nakamura, “Average-case linear-time similar substring searching by the q-gram distance.” Theoretical Computer Science, 530 (2014), 23-41.http://dx.doi.org/10.1016/j.tcs.2014.02.022

[Lu_2014] Guoliang Lu, Mineichi Kudo, “Learning action patterns in difference images for efficient action recognition”, Neurocomputing, 123-10 (2014), 328-336. http://dx.doi.org/10.1016/j.neucom.2013.06.042

Conference Papers

[Konno_2014] Hideaki Konno, Rinako Sato, Hideyuki Imai, Mineichi Kudo, “Deterioration of intelligibility in whispered Japanese speech.” Proc. of 2014 Annual Summit and Conference (APSIPA), 1-4.

[Tanaka_2014b] A Tanaka, I Takigawa, H Imai, M Kudo, “Theoretical Analyses on Ensemble and Multiple Kernel Regressors.” Proceedings of the Sixth Asian Conference on Machine Learning, 1-15.

[Tsukioka_2014] Hiroshi Tsukioka, Mineichi Kudo, “Selection of Features in Accord with Population Drift”, Proceeding of the 22nd International Conference on Pattern Recognition(ICPR2014), 2014, 1591-1596.

[Sasaki_2014] Daisuke Sasaki, Mineichi Kudo, “Stumble Detection using an Accelerometer in the Sole of a Shoe”, 2nd International Workshop on Pattern Recognition for Healthcare Analytics, 2014.

[Nishikawa_2014] Kenshiro Nishikawa, Mineichi Kudo, Group Sleepiness Measurement in Classroom. Activity Monitoring by Multiple Distributed Sensing,Lecture Notes in Computer Science. Vol. 8703, Pier Luigi Mazzeo et al.(eds.), Springer, 2014, 65-72. http://dx.doi.org/10.1007/978-3-319-13323-2_6

[Anton_2014]Anton Milan, Stefan Roth, Konrad Schindler and Mineichi Kudo, Privacy Preserving Multi-target Tracking. Computer Vision – {ACCV} 2014 Workshops – Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part III, 519-530.

[Mine_2014] P Stereo et al., “Homage to Professor Maria Petrou.”Pattern Recognition Letters 48, 2-7.

[Tanaka_2014a] Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, “Analyses on Generalization Error of Ensemble Kernel Regressors”, Structural, Syntactic, and Statistical Pattern Recognition (2014), 273-281.

[Kimura_2014] Kimura, Keigo, Yuzuru Tanaka, and Mineichi Kudo. “A Fast Hierarchical Alternating Least Squares Algorithm for Orthogonal Nonnegative Matrix Factorization.” Proceedings of the Sixth Asian Conference on Machine Learning. 2014.

[Atsu_2014] Atsuyoshi Nakamura,”A UCB-Like Strategy of Collaborative Filtering”, Proceedings of the Sixth Asian Conference on Machine Learning, 2014.

2013

Journal Papers

[Lu_2013a] Guoliang Lu and Mineichi Kudo, “Self-Similarities in Difference Images: A New Cue for Single-Person Oriented Action Recognition.” IEICE Transactions, 96-D(5): 1238-1242 (2013).

[Atsu_2013a] Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Mineichi Kudo and Hiroshi Mamitsuka, “Fast algorithms for finding a minimum repetition representation of strings and trees.” Discrete Applied Mathematics, 161(10-11), 1556-1575 (2013).

Conference Papers

[sada_2013a] Sadamori Koujaku, Mineichi Kudo, Ichigaku Takigawa and Hideyuki Imai, “Structual Change Point Detection for Social Networks.” Proceedings of International Conference of Computational Statistics and Data Engineering IAENG, London, pp. 324-329, 2013. (Best Student Award)DOI:10.1145/2740908.2742471

[Yingmei_2013] Yingmei Piao and Mineichi Kudo, “How Do Facial Expressions Contribute to Age Prediction ?.” Proc. of ACPR 2013, 882-886.

[Konnno_2013] Hideaki Konno, Hideo Kanemitsu, Nobuyuki Takahashi and Mineichi Kudo, “Acoustic characteristics related to the perceptual pitch in whispered vowels.” Proc. of ASRU 2013, 245-249.

[Endo_2013] Tomomi Endo and Mineichi Kudo, “Weighted Naïve Bayes Classifiers by Renyi Entropy.” Proc. of CIARP, 2013, 149-156.

Technical Reports

[Hanada_2013] 花田 博幸, 中村 篤祥, 工藤 峰一, 末尾への挿入が定数時間で可能な平衡二分探索木. 人工知能学会 第89回人工知能基本問題研究会, 2013, 25-30.

[Watanabe_2013a] 渡辺 僚, 中村 篤祥, 工藤 峰一, 順列バンディット問題に関する考察, ERATO湊離散構造処理系プロジェクト 2013初夏のワークショップ, 北海道, 2013年.

[Watanabe_2013b] 渡辺 僚, 中村 篤祥, 工藤 峰一, 順列バンディット問題における新しいUCB型アルゴリズム. コンピュテーション研究会, COMP2013-26. 鳥取県

[Watanabe_2013c] Ryo Watanabe, Atsuyoshi Nakamura and Mineichi Kudo, A New UCB-Like Algorithm for Permutation Bandit Problem. NIPS Workshop on Bayesian Optimization. Lake Tahoe, NV, US, Dec 2013.

2012

Journal Papers

[Tao_2012a] S. Tao, M. Kudo and H. Nonaka, “Privacy-preserved behavior analysis and fall detection by an infrared ceiling sensor network.” Sensors, 12(2012), pp. 16920-16936. DOI:10.3390/s121216920

[LU_2012a] G. Lu, M. Kudo and J. Toyama, Selection of characteristic frames in video for efficient action recognition, IEICE Transactions on Information and Systems, Vol.E95-D, No.10, pp.2514-2521, 2012.

[LU_2012b] G. Lu, M. Kudo and J. Toyama, Temporal segmentation and assignment of successive actions in a long-term video, Pattern Recognition Letters(2012), DOI:10.1016/j.patrec.2012.10.023

[Pan_2012] S. Pan, M. Kudo. “Recognition of Wood Porosity Based on Direction Insensitive Feature Sets.” Trans. MLDM 5(1): 45-62 (2012).

Conference Papers

[Tanaka_2012] A. Tanaka, I. Takigawa, H. Imai, M. Kudo, “Extended Analyses for an Optimal Kernel in a Class of Kernels with an Invariant Metric.” SSPR/SPR 2012: 345-353.

[Tao_2012b] S. Tao, M. Kudo and H. Nonaka, Privacy-preserved fall detection by an infrared ceiling sensor network. Proceedings of Biometrics Workshop, pp. 23-28, 2012.

[Hanada_2012] H. Hanada, A. Nakamura and M. Kudo, “Quasi-Linear-Time Substring Searching by q-gram Distance.” Proceedings of the 4th International Conference on Data Mining and Intelligent Information Technology Applications, pp. 540-545, 2012.

[Omura_2012] K. Omura, M. Kudo, T. Endo and T. Murai, Weighted Naive Bayes Classifier on Categorical Features. Proceedings of the 4th International Conference on Soft Computing and Pattern Recognition, pp. 865-870, 2012.

[Yasuda_2012] H. Yasuda, M. Kudo, Speech Rate Change Detection in Martingale Framework. Proceedings of the 4th International Conference on Soft Computing and Pattern Recognition, pp. 859-864, 2012.

[Tabata_2012] K. Tabata, A. Nakamura and M. Kudo, Fast Approximation Algorithm for the 1-Median Problem. Proceedings of the 15th International Conference on Discovery Science, pp. 169-183, 2012.

[LU_2012c] G. Lu, M. Kudo and J. Toyama, Action Recognition via Sparse Representation of Characteristic Frames. Proceeding of the 21st International Conference on Pattern Recognition (ICPR2012), 2012, 3268-3271.

[Tao_2012c] S. Tao, M. Kudo and H. Nonaka, Camera View Usage of Binary Infrared Sensors for Activity Recognition. Proceeding of the 21st International Conference on Pattern Recognition (ICPR2012), 2012, 1759-1762.

Technical Reports

[Endo_2011] 遠藤友美, 工藤峰一, サウンドリラクゼーションのための立体音響演出. 感性フォーラム札幌2012.

[Watanabe_2012a] 渡辺 僚, 中村 篤祥, 工藤 峰一, Bandit手法による制約条件下でのWeb広告選択. 第4回データ工学と情報マネジメントに関するフォーラム (DEIM2012). 2012年.

[Watanabe_2012b] 渡邊 僚, 中村 篤祥, 工藤 峰一, マッチング選択多腕Bandit問題の効率的解法アルゴリズム. 人工知能基本問題研究会(第86回), 2012.

2011

Journal Papers

[Tetsuji_2011] T. Takahasi, M. Kudo and A. Nakamura, Construction of Convex Hull Classifiers in High Dimensions. Pattern Recognition Letters, No. 32, pp. 2224-2230, 2011. DOI:10.1016/j.patrec.2011.06.020

[Kanemitsu_2011]金光秀雄・今野英明・宮腰政明・新保勝・工藤峰一「極小値が単峰列となる多峰関数の大域的最適化法(1) -単峰領域幅が等しい目的関数の大域的最適化-」. Vol.J94-A No.8, 604-620, 2011.

[Tane_2011] N. Taneichi, Y. Sekiya and J. Toyama, Improved Transformed Deviance Statistic for Testing a Logistic Regression Model. Journal of Multivariate Analysis, Vol. 102, Is. 9, pp. 1263-1279, 2011.

[Pan_2011a] S.Pan and M.Kudo, Segmentation of Pores in Wood Microscopic Images Based on Mathematical Morphology with a Variable Structuring Element. Computers and Electronics in Agriculture, Vol. 75, No. 2,250-260. DOI:10.1016/j.compag.2010.11.010

Conference Papers

[Shimbo_2011] M.Shimbo, J. Toyama, M. Shimbo, Cross-Modal Perception in the Framework of Non-Riemannian Sensory Space. Proceedings of the 12th International Multisensory Research Forum, 2011.

[Nonaka_2011] H. Nonaka, S. Tao, J. Toyama and M. Kudo, Ceiling Sensor Network for Soft Authentication and Person Tracking Using Equilibrium Line, Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems, pp. 218-223, 2011.

[Hanada_2011] H. Hanada, A. Nakamura and M. Kudo, A Practical Comparison of Edit Distance Approximation Algorithms, Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 231-236, 2011.

[Ouchi_2011] K. Ouchi, A. Nakamura and M. Kudo, Efficient Construction and Usefulness of Hyper-Rectangle Greedy cover, Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 533-538, 2011.

[Nakane_2011] H. Nakane, J. Toyama and M. Kudo, Fatigue Detection Using a Pressure Sensor Chair, Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 490-495, 2011.

[LU_2011a] Guoliang LU, Mineichi KUDO and Jun TOYAMA. Hierarchical Foreground Detection in Dynamic Background, Computer Analysis of Images and Patterns, 14th International Conference, Lecture Notes in Computer Science. Vol. 6855, Pedro Real et al. (eds.), Springer, 2011. 413-420. DOI: 10.1007/978-3-642-23678-5_49

[LU_2011b] Guoliang LU, Mineichi KUDO and Jun TOYAMA. Robust Human Pose Estimation from Corrupted Images with Partial Occlusions and Noise Pollutions, Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 433-438, 2011.

[Tao_2011a] S. Tao, M. Kudo, H. Nonaka and J. Toyama, Person Localization and Soft Authentication Using an Infrared Ceiling Sensor Network, Computer Analysis of Images and Patterns, 14th International Conference, Lecture Notes in Computer Science. Vol. 6855, Pedro Real et al. (eds.), Springer, pp. 122-129, 2011.

[Tao_2011b] S. Tao, M. Kudo, H. Nonaka and J. Toyama, Recording the Activities of Daily Living Based on Person Localization Using an Infrared Ceiling Sensor Network, Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 647-652, 2011.

[Tao_2011c] S. Tao, M. Kudo, H. Nonaka and J. Toyama, Person Authentication and Activities Analysis in an Office Environment Using a Sensor Network, AmI 2011 Workshops, Communications in Computer and Information Science. Vol. 277, R. Wichert, K. Van Laerhoven, J. Gelissen (Eds.), Springer, Heidelberg, 2012. 119-127.


[Omura_2011] K. Omura, K. Aoki, M. Kudo, Attribute Value Reduction for Gaining Simpler Rules, Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 527-532, 2011.

[Pan_2011b] S. Pan and M. Kudo, Recognition of porosity in wood microscopic anatomical images. Proceedings of the 11th Industrial Conference on Data Mining. pp. 147-160, 2011.

Technical Reports

[Omura_2011] 大村和広, 村井哲也, 工藤峰一, 決定ルールセットの有用性指標の提案と利用. 第27回ファジイシステムシンポジウム 2011.

2010

Journal Papers

[Tabata_2010] K. Tabata, M. Sato and M. Kudo, Data Compression by Volume Prototypes for Streaming Data. Pattern Recognition, Vol.43, No.9, 3162-3176. DOI:10.1016/j.patcog.2010.03.012

[jun_2010] J. Toyama, M. Kudo and H. Imai, Probably Correct k-Nearest Neighbor Search in High Dimensions. Pattern Recognition, Vol.43, No.4, 1361-1372. DOI:10.1016/j.patcog.2009.09.026 (The software is available at OpenSoftware)

Conference Papers

[kazu_2010] K. Aoki and M. Kudo, A Top-Down Construction of Class Decision Trees with Selected Features and Classifiers, Proceedings of the 2010 International Conference on High Performance Computing and Simulation, 2010, 390–398. DOI:10.1109/HPCS.2010.5547102

[takira_2010] A. Tanaka, H. Imai, M. Kudo and M. Miyakoshi, A Relationship Between Generalization Error and Training Samples in Kernel Regressors. Proceedings of the 20th International Conference on Pattern Recognition (ICPR2010), 2010, Istanbul, Turkey, 1421–1424.DOI:10.1109/ICPR.2010.351

[tetsuji_2010] T. Takahashi and M. Kudo, Margin Preserved Approximate Convex Hulls for Classification, Proceedings of the 20th International Conference on Pattern Recognition (ICPR2010), 2010, Istanbul, Turkey, 4052–4055. DOI:10.1109/ICPR.2010.985

[kazuki_2010] K. Tsuji, M. Kudo and A. Tanaka, Localized Projection Learning, Structural, Syntactic and Statistical Pattern Recognition, Lecture Notes in Computer Science. vol. 6218, Edwin R. Hancock et al. (eds.), Springer, 2010. 90-99. DOI:10.1007/978-3-642-14980-1_8 (The video is available at http://videolectures.net/ssspr2010_cesme/)

[uchiya_2010] T. Uchiya, A. Nakamura and M. Kudo, Algorithms for Adversarial Bandit Problems with Multiple Plays, Algorithmic Learning Theory, 21st International Conference, Lecture Notes in Artificial Intelligence, vol. 6331, Marcus Hutter et al. (eds.), Springer, 2010, 375–389. DOI:10.1007/978-3-642-16108-7

[Nakamura_2010] A. Nakamura,T. Saito, I. Takigawa, H. Mamitsuka, and M. Kudo, Algorithms for Finding a Minimum Repetition Representation of a String, String Processing and Information Retrieval, 17th International Symposium, Lecture Notes in Computer Science, vol. 6393, Edgar Chavez and Stefano Lonardi (eds.), Springer, 2010, 185–190. DOI:10.1007/978-3-642-16321-0

Technical Reports

[oouchi_2010] 大内康治,中村篤祥,工藤峰一,超矩形による貪欲被覆学習の効率的実装と実データによる性能評価. 電子情報通信学会技術研究報告, IBISML2010-60–IBISML-103(2010), Vol.110, No.265, 99-104.

[yanagi_2010] 柳堀慎吾・工藤峰一, 大規模データに対する識別子独立型の特徴選択. 電子情報通信学会技術報告, PRMU 2010-139(2010), 73-78.

[taneichi_2010] 種市信裕, 関谷祐里, 外山 淳,ロジスティック回帰モデルにおけるデビアンス統計量の改良について.科研費による研究集会「生物情報解析の理論的基礎とその応用」,2010.

[jun_2010] 外山淳,雑音混入音声の特徴空間における軌道を考慮した音声認識. 平成22年秋季音響学会研究発表会, 1-9-5, 2010.

[uemura_2010] 上村明仁,外山 淳,音源の個人性を考慮した声質変換.第12回 音声言語シンポジウム,2010.

2009

Journal Papers

[1gac_2009] I. Takigawa, M. Kudo and A. Nakamura, Convex sets as prototypes for classifying patterns. Engineering Applications of Artificial Intelligence. Vol.22, No.1, pp. 101-108, 2009.

[taisuke_08] T. Hosokawa, M. Kudo, H. Nonaka and J. Toyama, Soft Authentication Using an Infrared Ceiling Sensor Network. Pattern Analysis and Applications, Vol.12, No.3, pp.237-250,2009. DOI:10.1007/s10044-008-0119-9

[yamada_08] M. Yamada, K. Kamiya, M. Kudo, H. Nonaka and J. Toyama, Soft Authentication and Behavior Analysis Using a Chair with Sensors Attached: Hipprint Authentication. Pattern Analysis and Applications, Vol.12, No.3, pp.251-260,2009. DOI:10.1007/s10044-008-0124-z

Conference Papers

[mine_2009] M. Kudo and J. Toyama and H. Imai, A Fast Nearest Neighbor Method Using Empirical Marginal Distribution. Vol. LNCS 5712 (2009), Juan D. Velasquez, et al. (eds.), Springer, pp. 333-339.

[satoshi_2009] S. Shirai, M. Kudo and A. Nakamura, Comparison of Bagging and Boosting Algorithms on Sample and Feature Weighting. Multiple Classifier System, Vol. LNCS 5519, (2009), pp. 22-31, J.A. Benediktsson, J. Kittler and F. Roli, Springer

[kanda_2009] Y. Kanda, M. Kudo and H. Tenmoto, Hierarchical and Overlapping Clustering of Retrieved Web Pages. Recent Advances in Intelligent Information Systems, pp. 345–358.

[maico_2009] M. Sato, M. Kudo and J. Toyama, Clustering and Density Estimation for Streaming Data using Volume Prototypes. Proceedings of the 9th International Workshop on Pattern Recognition in Information Systems – PRIS 2009, Milan, Italy, 2009, pp. 39–48.

[tetsuji_2009] T. Takahashi, M. Kudo and A. Nakamura, Classifier Selection in a Family of Polyhedron Classifiers. Vol. LNCS 5856, E. Bayro-Corrochano and J.-O. Eklundh (eds.), Springer, 2009, 441-448. (Outstanding Presentation Award)

[pan_2009] S. Pan, M. Kudo and J. Toyama, Edge Detection of Tobacco Leaf Images Based on Fuzzy Mathematical Morphology. Proc. of the 1st International Conference on Information Science and Engineering (ICISE2009), Nanjing, China, 2009, CD-ROM:978560.

Technical Reports

[taneichi_2009] 種市信裕・関谷祐里・外山 淳,ロジスティック回帰分析の検定統計量の分布の漸近展開.シンポジウム統計科学における数理的手法の理論と応用,2009.

[uemura_2009] 上村明仁,外山 淳,声道特性と音源特性を考慮した声質変換.電気・情報関係学会北海道支部連合大会,2009.

[jun_2009a] 外山淳,雑音のパワー変動に応じたサブトラクション係数の推定. 平成21年春季音響学会研究発表会, 1-5-13, 2009.

[mine_2009a] 工藤峰一・今井英幸・田中 章・杉山 将, 「特別講演」パターン認識における都市伝説. 電子情報通信学会技術報告, PRMU 2009-142(2009), 29-34.

[uchiya_2009a] 打矢泰志,中村篤祥,工藤峰一,複数アクションを選択するAdversarial Bandit 問題について. 電子情報通信学会技術研究報告, COMP2009-25–COMP2009-31(2009), Vol.109, No.195, 13-20.

2008

Journal Papers

[mine_08a] M. Kudo and T. Murai, Extended DNF Expression and Variable Granularity in Information Tables. IEEE Trans. on Fuzzy Sets and Systems, 16-2(2008), 285-298.

[shidara_2008a] Y. Shidara, M. Kudo and A. Nakamura, Classification Based on Consistent Itemset Rules. Transactions on Machine Learning and Data Mining, 1-1(2008), 17-30.

Conference Papers

[takira_08a] A. Tanaka, H. Imai, J. Toyama, M. Kudo and M. Miyakoshi, Wiener Implementation of Kernel Machines. 5-th IASTED International Conference Signal Processing, Pattern Recognition, and Applications, Insbruck, 2008, 1-6.

[tosaka_2008] 戸坂 央,中村 篤祥,工藤 峰一, 類似部分森が頻出するパターン森の発見. 第22回人工知能学会全国大会論文集,2008,2B2-1.

[kazu_2008] K. Aoki and M. Kudo, Feature and Classifier Selection in Class Decision Trees. Structural, Syntactic and Statistical Pattern Recognition, Lecture Notes in Computer Science. vol. 5342, N. da Vitora Lobo et al. (eds.), Springer, 2008. 562-571.

[satoshi_2008] S. Shirai, M. Kudo and A. Nakamura, Bagging, Random Subspace Method and Biding. Structural, Syntactic and Statistical Pattern Recognition, Lecture Notes in Computer Science. Vol. 5342, N. da Vitora Lobo et al.(eds.), Springer, 2008, 811–820.

[sato_2008] Maiko Sato, Mineichi Kudo and Jun Toyama Behavior Analysis of Volume Prototypes in High Dimensionality. Structural, Syntactic and Statistical Pattern Recognition, Lecture Notes in Computer Science. vol. 5342, N. da Vitora Lobo et al. (eds.), Springer, 2008, 884–894.

[tenmo_2008] H. Tenmoto and M. Kudo, Soft Feature Selection by Using a Histogram-Based Classifier. Structural, Syntactic and Statistical Pattern Recognition, Lecture Notes in Computer Science. vol. 5342, N. da Vitora Lobo et al. (eds.), Springer, 2008, 582-591.

[takira_2008b] A. Tanaka, H. Imai, M. Kudo, and M, Miyakoshi, Optimal Kernel in a Class of Kernels with an Invariant Metric. Structural, Syntactic and Statistical Pattern Recognition, Lecture Notes in Computer Science. vol. 5342, N. da Vitora Lobo et al. (eds.), Springer, 2008, 530-539.

[shidara_2008b] Y. Shidara, M. Kudo and A. Nakamura, Classification by Bagged Consistent Itemset Rules. Proceedings of the 19th International Conference on Pattern Recognition (ICPR2008), Tampa, Florida, USA.

[kami_2008a] K. Kamiya, M.Kudo, H. Nonaka and J. Toyama, Sitting Posture Analysis by Pressure Sensors. Proceedings of the 19th International Conference on Pattern Recognition (ICPR2008), Tampa, Florida, USA.

[mine_2008a] M. Kudo, I. Takigawa and A. Nakamura, Classification by Reflective Convex Hulls. Proceedings of the 19th International Conference on Pattern Recognition (ICPR2008), Tampa, Florida, USA.

[atsu 2008] A. Nakamura, M. Kudo, What Sperner Family Concept Class is Easy to Be Enumerated? Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008, 482-491.

Technical Reports

[kanda_2008a] 神田勇介,工藤峰一,階層的重複クラスタリングによるウェブ検索結果の集約. 電子情報通信学会技術研究報告, PRMU 2008-184(2008), 219-224.

[uchiya_2008a] 打矢泰志,中村篤祥,工藤峰一,非確率的なmulti-armed bandit問題における分散投資の効果について. 電子情報通信学会技術研究報告, PRMU 2008-183(2008), 213-218.

[tetsuji_2008a] 高橋哲自,工藤峰一,中村篤祥,多面体識別子族における識別子選択. 電子情報通信学会技術研究報告, PRMU 2008-153(2008), 37-42.

[haya_2008a] 木下隼人,外山淳,音響管モデルに束縛を与えた音声合成. 電子情報通信学会技術研究報告, SP 2008-57(2008), 7-11.

[jun_2008b] 外山淳,雑音のパワー変動と位相を考慮した雑音環境下の単語認識. 平成20年秋季音響学会研究発表会, 1-1-3, 2008.

[jun_2008a] 外山淳,SNRの変化に適応するSS法による雑音混入音声の認識. 電子情報通信学会技術研究報告, SP 2007-169(2008), 13-18.

[hanada_2008a] 花田博幸,工藤峰一,編集距離による最類似文字列の探索高速化に関する研究.電子情報通信学会技術研究報告,108, 93(2008), 41-45.

2007

Journal Papers

[takira_2007a] A. Tanaka, H. Imai, M. Kudo, and M. Miyakoshi, Integrated Kernels and Their Properties. Pattern Recognition, 40(2007), 2930-2938.

Conference Papers

[tosaka_ds07] H. Tosaka, A. Nakamura and M. Kudo, Mining Subtrees with Frequent Occurrence of Similar Subtrees. Discovery Science, Vol. LNAI 4755, V. Corruble, M. Takeda, E. Suzuki (eds.), Springer, 2007. 286-290.

[shidara_mldm07] Y. Shidara, A. Nakamura and M. Kudo, CCIC: Consistent Common Itemsets Classifier. Machine Learning and Data Mining in Pattern Recognition, Vol. LNAI 4571, P. Perner (ed.), Springer, 2007. 409-498.

[muto_jrs07] Y. Muto, M. Kudo and Y. Shidara, Reduction of Categorical and Numerical Attribute Values for Understandability of Data and Rules. Rough Sets and Knowledge Technology, Vol. LNAI 4481, J. Yao, P. Lingras, et al. (eds.), Springer, 2007. 211-218.

[mine_mcs07] M. Kudo, S. Shirai and H. Tenmoto, A Combination of Sample Subsets and Feature Subsets in One-Against-Other Classifiers. Multiple Classifier System, Vol. LNCS 4472, M. Haindl, J. Kittler and F. Roli (eds.), Springer, 2007. 241-250.

[hayashi_2007] 林真吾・工藤峰一, 手書きの趣を活かしたペン入力インタフェース. ヒューマンインタフェースシンポジウム2007論文集, 2007. 313-318.

Technical Reports

[tosaka_2007a] 戸坂央・中村篤祥・工藤峰一, 木構造データに対する頻出類似部分木の発見. 電子情報通信学会技術研究報告, PRMU 2007-28(2007), 7-12.

[kamiya_2007a] 紙谷一啓・工藤峰一・野中秀俊・外山淳,圧力センサを用いた着席者の姿勢識別に関する研究. 情報処理学会研究報告, UBI 2007-74(2007), 41-46.

[satoshi_2007a] 白井賢志・工藤峰一, データ部分集合と特徴部分集合の同時選択による識別子統合. 電子情報通信学会技術研究報告, PRMU 2007-39(2007), 69-74.

[maico_2007a] 佐藤麻衣子・工藤峰一・外山淳, 体積プロトタイプの解析と混合分布モデルとの比較. 電子情報通信学会技術研究報告, PRMU 2007-43(2007), 93-98.

2006

Journal Papers

[muto_2006a] Y. Muto, M. Kudo and T. Murai, Reduction of Attribute Values for Kansei Representation, Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), 10-5(2006), 666-672.

[naoto_2006a] N. Abe and M. Kudo, Non-Parametric Classifier-Independent Feature Selection. Pattern Recognition, 39(2006), 737-746.

[naoto_2006b] N. Abe, M. Kudo, J. Toyama and M. Shimbo, Classifier-Independent Feature Selection on the Basis of Divergence Criterion. Pattern Analysis and Applications, 9(2006), 127-137.

Conference Papers

[takira_2006a] A. Tanaka, M. Sugiyama, H. Imai, M. Kudo and M. Miyakoshi, Model Selection Using a Class of Kernels with an Invariant Metric Structural, Syntactic and Statistical Pattern Recognition, Lecture Notes in Computer Science. vol. 4109, D. Y. Yeung, J. T. Kwok, A. Fred, F. Roli and D. Ridder (eds.), Springer, 2006. 862-870.

[masa_2006a] M. Yamada, M. Kudo, H. Nonaka and J. Toyama, Hipprint Person Identification and Behavior Analysis. Proceedings of the 18th International Conference on Pattern Recognition (ICPR2006), Hong Kong, 2006, CD-ROM D04_0334. (4p.)

[jun_2006a] Hideaki Konno, Hideo Kanemitsu, Jun Toyama and Masaru Shimbo, Spectral properties of Japanese whispered vowels referred to pitch. 4th joint meeting of the Acoustical Society of America and the Acoustical Society of Japan, Honolulu, USA, 2006.

[atsu 2006] A. Nakamura, Learning-Related Complexity of Linear Ranking Functions. Proceedings of the 17th International Conference on Algorithmic Learning Theory (ALT 2006), 2006, 378-392.

Technical Reports

[mine_2006a] 工藤峰一, 「特別講演」特徴選択 ~これまでと今後の展開~. 電子情報通信学会技術報告, PRMU 2006-167(2006), 37–42.

[muto_2006b] 武都祐司・工藤峰一, 情報の粒度を用いた属性値の抽象化. 電子情報通信学会技術報告, PRMU 2006-174(2006), 19–24.

[tabata_2006a] 田端健志・工藤峰一, 体積プロトタイプによるデータ集約. 電子情報通信学会技術報告, PRMU 2006-175(2006), 25–30.

2005

Journal Papers

[atsu_2005d] A. Nakamura, N. Abe, Improvements to the Linear Programming Based Scheduling of Web Advertisements. Electronic Commerce Research 5(1), 2005, 75-98.

[mich_2005] A. Nakamura, M. Schmitt, N. Schmitt, H. Simon, Inner Product Spaces for Bayesian Networks. Journal of Machine Learning Research 6, 2005, 1383-1403.

[atsu_2005a] A. Nakamura, An efficient query learning algorithm for ordered binary decision diagrams. Information and Computation 201(2), 2005, 178-198.

[kawata_2005] 河田 岳大・工藤 峰一・中村 篤祥・外山 淳, 両方向N-gram確率を用いた誤り文字検出法. 電子情報通信学会論文誌,J88-D-II-3(2005), 629-635.

Conference Papers

[choku_2005] N. Abe and M. Kudo Entropy Criterion for Classifier-Independent Feature Selection. Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, Vol. 3684, R. Khosla, R. J. Howlett, L. C. Jain (eds.), Springer, 2005, 689–695.

[hase_2005a] 長谷川 博之, 工藤 峰一, 中村 篤祥, 構造と内容に基づくWebページからの評判抽出におけるパターンの構成法. Proc. of Data Engineering Workshop 2005, 5C-o4.

[hase_2005b] H. Hasegawa, M. Kudo, A. Nakamura, Empirical Study on Usefulness of Algorithm SACwRApper for Reputation Extraction from the WWW. Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, Vol. 3684, Rajiv Khosla, Robert J. Howlett, Lakhmi C. Jain (Eds.), Springer, 2005, 668-674.

[tai_2005] T. Hosokawa, M. Kudo, Person Tracking with Infrared Sensors. Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, Vol. 3684, Rajiv Khosla, Robert J. Howlett, Lakhmi C. Jain (Eds.), Springer, 2005, 682-688.

[ino_2005a] 伊野 秀彦, 工藤 峰一, 中村 篤祥, コミュニティ集合族発見手法の比較. Proc. of Data Engineering Workshop 2005, 5C-i5.

[ino_2005b] H. Ino, M. Kudo, and A. Nakamura, A Comparative Study of Algorithms for Finding Web Communities. Proc. of the International Special Workshop on Databases for Next Generation Researchers, 2005, 154-157.

[atsu_2005a] H. Ino, M. Kudo, and A. Nakamura, Partitioning of Web graphs by community topology. Proc. of WWW2005, 661-669.

[mine_fsdm2005] M. Kudo and H. Tenmoto, Optimal Division for Feature Selection and Classification. Proceedings of the Workshop on Feature Selection for Data Mining: Interfacing Machine Learning and Statistics, Newport Beach, 2005, April, 106–107.

[mine_2005b] M. Kudo and T. Murai, A New Treatment and Viewpoint of Information Tables}. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), Lecture Notes in Artificial Intelligence, Vol. 3641, D. Slezak, G. Wang, M. Szczuka, I. Duntsch, Y. Yao (eds.), Springer, 2005. 234–243.

[muto_2005] Y. Muto and M. Kudo, Discernibility-Based Variable Granularity and Kansei Representations. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), Lecture Notes in Artificial Intelligence}, Vol. 3641, D. Slezak, G. Wang, M. Szczuka, I. Duntsch, Y. Yao (eds.), Springer, 2005. 692–700.

[atsu_2005b] A. Nakamura and M. Kudo, Mining Frequent Trees with Node-Inclusion Constraints. Proc. of PAKDD 2005, 850-860.

[temmo_2005a] H. Tenmoto and M. Kudo, Density- and Complexity- Regularization in Gaussian Mixture Bayesian Classifier. Proceedings of the 4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology, Advances in Soft Computing, Soft Computing as Transdisciplinary Science and Technology, A. Abraham, et al. (Eds.), Springer, 2005, 391-399.

[masa_2005] M. Yamada, J. Toyama, M. Kudo, Person Recognition by Pressure Sensors. Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, Vol. 3684, Rajiv Khosla, Robert J. Howlett, Lakhmi C. Jain (Eds.), Springer, 2005, 703-708.

[michal_2005] Haindl, M. and Grim, J. and Pudil, P. and Kudo M., A Hybrid BTF Model Based on Gaussian Mixtures. Texture 2005. The 4th International Workshop on Texture Analysis and Synthesis in conjunction with ICCV2005, Chantler, M. and Drbohlav, O. (eds.), Heriot-Watt University & IEEE, 2005, 95-100.

Technical Reports

[atsu_2005b]中村篤祥, ランキング関数のオンライン学習について. 計算機科学基礎理論とその応用 2005冬, 京都大学数理解析研究所講究録1426, 51-56.

[atsu_2005c] A. Nakamura, How Easy to Learn Linear Ranking Functions. 電子情報通信学会技術報告書, COMP2005-35(2005), 49-53.

[atsu_2005c] H. Hasegawa, M. Kudo and A. Nakamura, Reputation Extraction Using Both Structural and Content Information. Hokkaido university TCS Technical Report Series A TCS-TR-A-05-2, 2005.

[jun_2005a] 今野英明,金光秀雄,高橋伸幸,外山淳,新保 勝, Webデータベース汎用システムの開発と音声データ管理システムとしての利用. 電子情報通信学会技術研究報告, PRMU2005-37(2005), 19-24.

Book Chapters

[Shidara05] Y. Shidara, M. Kudo and A. Nakamura, Extraction of Generalized Rules with Automated Attribute Abstraction, Foundations of Data Mining and knowledge Discovery, Studies in Computational Intelligence, Vol. 6, T. Y. Lin, S. Ohsuga, C-J Liau, X. Hu, S. Tsumoto (eds.), Springer-Verlag GmbH, 2005, 161–170. DOI:10.1007/11498186_10

2004

Journal Papers

[1gac_2004a] I. Takigawa, M. Kudo and J. Toyama, Performance Analysis of Minimum L1-Norm Solutions for Underdetermined Source Separation. IEEE Transactions on Signal Processing}, 52-3(2004), 582-591.

[1gac_2004b] I. Takigawa, N. Abe, Y. Shidara and M. Kudo, The Boosted/Bagged Subclass Method. International Journal of Computing Anticipatory Systems. 14(2004), 311-320.

Conference Papers

[michal_icpr2004] M. Haindl, J. Grim, P. Somol, P. Pudil and M. Kudo,A Gaussian Mixture-Based Colour Texture Model. Proceedings of the 17th International Conference on Pattern Recognition (ICPR2004), Cambrige, U.K., 2004, CD-ROM.

[mine_sci2004]: M. Kudo, T. Hosokawa, J. Toyama, H. Tenmoto, and A. Nakamura, Person Identification with Environment Information. Proceedings of the Eeighth World Multiconference on Systemics, Cybernetics and Informatics (SCI’2004), Orlando, 2004, Vol. V, 65-68.

[mine_spr2004] M. Kudo, H. Imai, A. Tanaka and T. Murai, A Nearest Neighbor Method Using Bisectors. Structural, Syntactic and Statistical Pattern Recognition, Lecture Notes in Computer Science, Vol. 3138, A. Fred, T. Caelli, R. P. W. Duin, A. Campilho, and D.Riddr (eds.), Springer, 2004. 885-893.

[atsu_2004a] 中村篤祥, 工藤峰一, コミュニティトポロジーによるWebグラフ分割. 第4回データマイニングワークショップ資料, 2004, 57-64.

[mich_2004] A. Nakamura, M. Schmitt, N. Schmitt, H. Simon, Bayesian Networks and Inner Product Spaces. Proc. of COLT 2004, 518-533.

[1gac_ica2004] I. Takigawa, M. Kudo, A. Nakamura and J.Toyama, \newblock On the Minimum L1-Norm Signal Recovery in Underdetermined Source Separation. Independent Component Analysis and Blind Signal Separation, Lecture Notes in Computer Science, Vol. 3195, C.G.Puntonet and A.Prieto (eds.), Springer, 2004, 193-200.

[tanaka_2004] A. Tanaka, I. Takigawa, H. Imai, M. Kudo and M. Miyakoshi, Projection Learning Based Kernel Machine Design Using Series of Monotone Increasing Reproducing Kernel Hilbert Spaces. Knowledge-Based Intelligent Information and Engineering Systems, Lcture Notes in Computer Science, Vol. 3213, Mircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain (eds.), Springer, 2004. 1058-1064.

[tenmoto_spr2004] H. Tenmoto, Y. Mori and M. Kudo, Classifier-Independent Visualization of Supervised Data Structure Using a Graph. Structural, Syntactic and Statistical Pattern Recognition, Lecture Notes in Computer Science, Vol. 3138, A. Fred, T. Caelli, R. P. W. Duin, A. Campilho, and D.Riddr (eds.), Springer, 2004. 1043-1051.

[masa_2004]: M. Yamada and M. Kudo, Combination of Weak Evidences by D-S Theory for Person Recognition. Knowledge-Based Intelligent Information and Engineering Systems, Lcture Notes in Computer Science, Vol. 3213, Mircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain (eds.), Springer, 2004, 1065-1071.

Technical Reports

[atsu_2004b] A. Nakamura and M. kudo, Mining Frequent Trees with Nnode-Inclusion Constraints. 電子情報通信学会技術報告書, COMP2004-44(2004),7-14.

2003

Journal Papers

[Mine03a] M. Kudo, N. Masuyama and M. Shimbo, Simple termination conditions for k-nearest neighbor method, Pattern Recognition Letters, 24(2003), 1213–1223.

[Yasu03a] Y. Mori, M. Kudo, J. Toyama and M. Shimbo, Comparison of Low-Dimensional Mapping Techniques Based on Discriminatory Information, International Journal of Knowledge-Based Intelligent Enginnering Systems, 7-2(2003), 70–77.

[Kazu03] 青木 和昭, 渡辺 俊晴, 工藤 峰一, クラスに依存した特徴集合を用いた決定木の設計, 電子情報通信学会論文誌, J86-D-II-8(2003), 1156–1165.

[Yasu03b] 森 康久仁, 工藤 峰一, グラフによるインタラクティブなデータ分析と決定木の構成, 電子情報通信学会論文誌, J86-D-II-8(2003), 1166–1176.

Conference Papers

[atsu_2003a] A. Nakamura, M. Kudo, A. Tanaka, Collaborative Filtering Using Restoration Operators. Proc of PKDD 2003, 339-349.

[atsu_2003b] A. Nakamura, M. Kudo, A. Tanaka, K. Tanabe, Collaborative Filtering Using Projective Restoration Operators. Porc. of Discovery Science 2003, 393-401.

Technical Reports

[Kawata03] 河田 岳大, 中村 篤祥, 外山 淳, 工藤 峰一, 両方向N-gram確率を用いた確率変化パターンによる誤り検出, 電子情報通信学会技術報告, PRMU 2003-75(2003), 1–5.

[Shidara03] 設樂洋爾, 中村篤祥, 工藤峰一, ルールの予測精度と興味深さに関する検討, 電子情報通信学会技術報告, PRMU 2003-76(2003), 7–11.

[Mine03b] 工藤峰一, 包含と排除によるk最近隣法の高速化, 電子情報通信学会技術報告, PRMU 2003-90(2003), 91–95.

2002

Journal Papers

[atsu_2002] A. Nakamura, N. Abe, Online Learning of Binary and n-ary Relations over Clustered Domains. J. Comput. Syst. Sci. 65(2), 2002, 224-256.

Conference Papers

[Abe02] N. Abe, M. Kudo and M. Shimbo, Classifier-Independent Feature Selection Based Non-parametric Discriminant Analysis, Advances in Pattern Recognition, Lecture Notes in Computer Science, Vol. 2396, Terry Caelli, Adnan Amin, Robert P. W. Duin, Mohamed Kamel, Dick de Ridder(Eds.), Springer, 2002, 470-479. (Proceedings of Joint IAPR International Workshops SSPR2002 and SPR2002, Windsor, Canada, August 6-9, 2002)

[Kazu02] K. Aoki and M. Kudo, Decision Tree Using Class-Dependent Feature Subsets, Advances in Pattern Recognition, Lecture Notes in Computer Science, Vol. 2396, Terry Caelli, Adnan Amin, Robert P. W. Duin, Mohamed Kamel, Dick de Ridder(Eds.), Springer, 2002, 761-769. (Proceedings of Joint IAPR International Workshops SSPR2002 and SPR2002, Windsor, Canada, August 6-9, 2002)

[Mine02] M. Kudo, Automatic Determination of Size for Feature Selection, Proceedings of the Sixth World Multiconference on Systemics, Cybernetics and Informatics, Orlando, 2002, Vol. XVI, 305-311.

[Yasu02] Y. Mori and M. Kudo, Interactive Data Exploration Using Graph Representation, Proceedings of the Sixth World Multiconference on Systemics, Cybernetics and Informatics (SCI’2002), Orlando, 2002, Vol. XVI, 312-317.

[atsu_2002] A. Nakamura, Improvements in practical aspects of optimally scheduling web advertising. Proc. of WWW 2002, 536-541.

[Ftani02] F. Taniguchi and M. Kudo, Random Selection of Samples and Features for Getting General Accuracy of Classifier Combination, Proceedings of the Sixth World Multiconference on Systemics, Cybernetics and Informatics (SCI’2002), Orlando, 2002, Vol. XVI, 329-332.

Book Chapters

[mine_prsm] M. Kudo, A Region-Based Algorithm for Classifier-Independent Feature Selection. Pattern Recognition and String Matching, D. Chen and Xi. Cheng (eds.), Kluwer Academic Publishers, 2002. 315–340.

2001

Journal Papers

[Haya_2001] H. Hayashi, M. Kudo, J. Toyama and M. Shimbo, “Fast Labeling of Natural Scenes Using Enhanced Knowledge.” Pattern Analysis and Applications, 4(2001), 20-27.

[Masu01] 益山 直人・工藤峰一・外山 淳・新保 勝, パターン認識問題における終端条件の付加によるk近隣法の高速化, 電子情報通信学会論文誌,J84-D-II-3(2001), 439-447.

Conference Papers

[Mine01] M. Kudo, T. Murai and M. Shimbo, Clustering Consistent with Human Perception, Proceedings of the Second International ICSC Symposium on Advances in Intelligent Data Analysis (AIDA’2001) (CDROM), Bangor, 2001, paper 1724-168.

[Yasu01] Y. Mori, M. Kudo, J. Toyama and M. Shimbo, Comparison of Low-Dimensional Mapping Techniques Based on Discriminatory Information, Proceedings of the Second International ICSC Symposium on Advances in Intelligent Data Analysis (AIDA’2001) (CDROM), Bangor, 2001, paper 1724-166.

[1gac01] I. Takigawa, M. Kudo, J. Toyama and M. Shimbo, Error Analysis of MAP Solutions under Laplace Prior in Underdetermined Blind Source Separation, Proceedings of the Second International ICSC Symposium on Advances in Intelligent Data Analysis (AIDA’2001) (CDROM), Bangor, 2001, paper 1724-169.

[Tenmo01] H. Tenmoto, Y. Mori, M. Kudo and M. Shimbo, Visualization of High-Dimensional Supervised Data Structure using Piecewise Linear Classifiers, Proceedings of the Second International ICSC Symposium on Advances in Intelligent Data Analysis (AIDA’2001) (CDROM), Bangor, 2001, paper 1724-167.

-2000

Journal Papers

[Mine00a] M. Kudo and J. Sklansky, Comparison of Algorithms that Select Features for Pattern Classifiers, Pattern Recognition, 33-1(2000), 25-41.

[atsu_00a] A. Nakamura, Query learning of bounded-width OBDDs. Theor. Comput. Sci. 241(1-2), 2000, 83-114.

[Mine99] M. Kudo, J. Toyama and M. Shimbo, Multidimensional Curve Classification Using Passing-Through Regions, Pattern Recognition Letters, 20-11-13(1999), 1103-1111.

[marc_99] M. Langheinrich, A. Nakamura, N. Abe, T. Kamba, Y. Koseki, Unintrusive Customization Techniques for Web Advertising. Computer Networks 31(11-16), 1999, 1259-1272.

[Mine98a] M. Kudo, Y. Torii, Y. Mori and M.Shimbo, Approximation of Class Regions by Quasi Convex Hulls. Pattern Recognition Letters, 19-9(1998), 777-786.

[Mine98b] M.Kudo and J. Sklansky, A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers. Kybernetika, 34(1998), 429-434.

[Sato98a] M. Sato, M.Kudo, J. Toyama and M. Shimbo, Construction of a Nonlinear Discrimination Function Based on the MDL Criterion. Kybernetika, 34(1998), 467-472.

[Tenmo98a] H. Tenmoto, M.Kudo and M. Shimbo, Piecewise Linear Classifiers Preserving High Local Recognition Rates. Kybernetika, 34(1998), 479-484.

[Tenmo98b] H. Tenmoto, M. Kudo and M. Shimbo, Piecewise Linear Classifiers with An Appropriate Number of Hyperplanes. Pattern Recogntion, 31-11(1998), 1627–1634.

[atsu_98a] A. Nakamura, J. Takeuchi, N. Abe, Efficient Distribution-Free Population Learning of Simple Concepts. Ann. Math. Artif. Intell. 23(1-2), 1998, 53-82.

[Li_98] X. Z. Li, M. Kudo, J. Toyama and M. Shimbo, “Knowledge-Based Enhancement of Low Spatial Resolution Images.” IEICE Transactions on Information and Systems, Vol. E-81-D, 5(1998), 457-463.

[Mine96a] M. Kudo, K. Mizukami, Y. Nakamura and M. Shimbo, Realization of Membership Queries in Character Recognition, Pattern Recognition Letters, 17(1996), 77-82.

[Mine96b] M. Kudo, S. Yanagi and M. Shimbo, Construction of Class Regions by a Randomized Algorithm: A Randomized Subclass Method, Pattern Recognition, 29(1996), 581-588.

[atsu_95a] A. Nakamura, N. Abe, Exact Learning of Linear Combinations of Monotone Terms from Function Value Queries. Theor. Comput. Sci. 137(1), 1995, 159-176.

[Mine92] M. Kudo and M. Shimbo, Supplementary Learning of Discrimination Rules Using Oracles and Queries in Concept Learning. Advances in Structural and Systactic Pattern Recognition, H. Bunke (Eds.), Series in Machine Perception and Artificial Intelligence, 5(1992), 141–150.

Conference Papers

[atsu_00b] A. Nakamura, N. Abe, H. Matoba, K. Ochiai, Automatic recording agent for digital video server. ACM Multimedia 2000, 57-66.

[Tenmo00] H. Tenmoto, M. Kudo and M. Shimbo, Selection of the Number of Components Using a Genetic Algorithm for Mixture Model Classifiers, Advances in Pattern Recognition, Lecture Notes in Computer Science, Vol. 1876, F. J. Ferri, J. M. Inesta, A. Amin, and P. Pudil (Eds.), Springer, 2000, 511-520. (Proceedings of Joint IAPR International Workshops SSPR2000 and SPR2000, Alicante, Spain, August/September, 2000)

[Abe00] N. Abe, M. Kudo, J. Toyama and M. Shimbo, A Divergence Criterion for Classifier-Independent Feature Selection, Advances in Pattern Recognition, Lecture Notes in Computer Science, Vol. 1876, F. J. Ferri, J. M. Inesta, A. Amin, and P. Pudil (Eds.), Springer, 2000, 668-676. (Proceedings of Joint IAPR International Workshops SSPR2000 and SPR2000, Alicante, Spain, August/September, 2000)

[Mine00b] M. Kudo, P. Somol, P. Pudil and M. Shimbo, Comparison of Classifier-Specific Feature Selection Algorithm, Advances in Pattern Recognition, Lecture Notes in Computer Science, Vol. 1876, F. J. Ferri, J. M. Inesta, A. Amin, and P. Pudil (Eds.), Springer, 2000, 677-686. (Proceedings of Joint IAPR International Workshops SSPR2000 and SPR2000, Alicante, Spain, August/September, 2000)

[Mine00c] M. Kudo, H. Imai and M. Shimbo, A Histogram-Based Classifier on Overlapped Bins, Proceedings of 15th International Conference on Pattern Recognition (ICPR2000), Vol. 2, Bercelona, September 3-7, 2000, 29-33.

[Mine00d] M. Kudo, H. Imai, T. Murai and M. Shimbo, An MDL-Based Classifier for Multidimensional Space, Proceedings of the 4th World Multiconference on Systemics, Cybernetics and Informatics (SCI’2000), Orlando, 2000, Vol. 3, 498-503.

[Mine00e] T. Murai, M. Kudo and Y.Sato, Discovery of Association Rules and Rough-Set-Based Concept Learning, Proceedings of the 4th World Multiconference on Systemics, Cybernetics and Informatics (SCI’2000), Orlando, 2000, Vol. 3, 504-508.

[nabe_99] N. Abe, A. Nakamura, Learning to Optimally Schedule Internet Banner Advertisements. Proc. of ICML 1999, 12-21.

[Tenmo99] H. Tenmoto, M. Kudo and M. Shimbo, Determination of the Number of Components Based on Class Separability in Mixture-Based Classifiers. Proceedings of 3rd International Conference on Conventional and Knowledge-Based Intelligent Electronic Systems(KES-99), Aderade, Aug. 31 – Spt. 1, 1999, 439-442.

[Masu99] N. Masuyama, M. Kudo, J. Toyama and M. Shimbo, Termination Conditions for a Fast k-Nearest Neighbor Method, Proceedings of 3rd International Conference on Conventional and Knowledge-Based Intelligent Electronic Systems(KES-99), Aderade, Aug. 31 – Spt. 1, 1999, 443-446.

[Koni99] J. Konishi, S. Simba, J. Toyama, M. Kudo and M. Shimbo, Tabu Search for Solving Optimization Problems on Hopfield Neural, Proceedings of 3rd International Conference on Conventional and Knowledge-Based Intelligent Electronic Systems(KES-99), Aderade, Aug. 31 – Spt. 1, 1999, 518-521.

[atsu_99] A. Nakamura, Learning Specialist Decision Lists. Proc. of COLT 1999, 215-225.

[Haya_99] H. Hayashi, M. Kudo, J. Toyama, and M. Shimbo, “Estimation of Velocity Vectors from a Video Stream Using Discontinuity of Optical Flow.” Proceedings of Third International Conference on Knowledge-Based Intelligent Information Engineering Systems(KES’99), Adelaide, 1999, 447-450.

[Kawa_99] M. Kawakami, M. Kudo, J. Toyama and M. Shimbo, “Effective Sampling Points for Two-Channel Spline Image Coding.” Proceedings of Third International Conference on Knowledge-Based Intelligent Information Engineering Systems(KES’99), Adelaide, 1999, 451-454.

[Gotoh_99] T. Gotoh, M. Kudo, J. Toyama, M. Shimbo, “Geometry Reconstruction of Urban Scenes by Tracking Vertical Edges. “Proceedings of Third International Conference on Knowledge-Based Intelligent Information Engineering Systems(KES’99), Adelaide, 1999, 455-458.

[atsu_98b] A. Nakamura, N. Abe, Collaborative Filtering Using Weighted Majority Prediction Algorithms. Proc. of ICML 1998, 395-403.

[Mine98c] M. Kudo, F. Taniguchi, H. Tenmoto and M. Shimbo, Appropriate Initial Component Densities of Mixture Modeling for Pattern Recognition, Proceedings of 2nd International Conference on Conventional and Knowledge-Based Intelligent Electronic Systems(KES’98), Aderade, 1998, April, 373-377.

[Mine98e] M. Kudo and J. Sklansky, Classifier-Independent Feature Selection for Two-stage Feature Selection. Advances in Pattern Recognition, Lecture Notes in Computer Science, Vol. 1451, A. Amin, D. Dori, P. Pudil and H. Freeman (Eds.), Springer, 1998, 548-554.(Proceeding of Joint IAPR International Workshops SSPR’98 and SPR’98, Sydney, Australia, August, 1998)

[Sato98b] M. Sato, M.Kudo, J. Toyama and M. Shimbo, Feature Selection for a Nonlinear Classifier.Advances in Pattern Recognition, Lecture Notes in Computer Science, Vol. 1451, A. Amin, D. Dori, P. Pudil and H. Freeman (Eds.), Springer, 1998, 555-563. (Proceeding of Joint IAPR International Workshops SSPR’98 and SPR’98, Sydney, Australia, August, 1998)

[Tenmo98d] H. Tenmoto, M. Kudo and M. Shimbo, MDL-based selection of the number of components in mixture models for pattern classification. Advances in Pattern Recognition, Lecture Notes in Computer Science, Vol. 1451, A. Amin, D. Dori, P. Pudil and H. Freeman (Eds.), Springer, 1998. 831-836. (Proceeding of Joint IAPR International Workshops SSPR’98 and SPR’98, Sydney, Australia, August, 1998)

[Mine98f] M. Kudo, H. Tenmoto, S. Sumiyoshi and M. Shimbo, A Subclass-Based Mixture Model for Pattern Recognition. Proceedings of 14th International Conference on Pattern Recognition (ICPR98), Vol. 1, Brisban, August 16-20, 1998, 870-872.

[Yasu98] Y. Mori, M. Kudo, J. Toyama and M. Shimbo, Visualization of the Structure of Classes Using a Graph. Proceedings of 14th International Conference on Pattern Recognition (ICPR98), Vol. 2, Brisban, August 16-20, 1998, 1724-1727.

[nabe_98] N. Abe, H. Mamitsuka, A. Nakamura, Empirical Comparison of Competing Query Learning Methods. Proc. fo Discovery Science 1998, 387-388.

[Zheng_98a] L. X. Zheng, M. Kudo, J. Toyama and M. Shimbo, Enhancing AVHRR Imagery to Estimate NDVI. Proceedings of International Conference on Signal Processing and Communications, Canary Islands, 1998, February 11-14, 169-172.

[Zheng_98b]L. X. Zheng, M. Kudo, J. Toyama and M. Shimbo, Enhancement of Low Spatial Resolution Image with Wavelet Transform. Proceedings of First International Conference on Geospatial Information in Agriculture and Forestry, Vol. I, Florida, 1998, June 1-3, 613-620.

[Mine97] M. Kudo and J. Sklansky, A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers. Proceedings of 1st International Workshop on Statistical Techniques in Pattern Recognition (STIPR’97), Prague, 1997, 91-96.

[Sato97] M. Sato, M.Kudo, J. Toyama and M. Shimbo, Construction of a Nonlinear Discrimination Function Based on the MDL Criterion. Proceedings of 1st International Workshop on Statistical Techniques in Pattern Recognition (STIPR’97), Prague, 1997, 141-146.

[Tenmo97] H. Tenmoto, M.Kudo and M. Shimbo, Piecewise Linear Classifiers Preserving High Local Recognition Rates. Proceedings of 1st International Workshop on Statistical Techniques in Pattern Recognition (STIPR’97), Prague, 1997, 171-176.

[Ftani97a] F. Taniguchi, M. Kudo, T. Murai and M. Shimbo, A Rough-Set-Based Approach to Estimation of Class Regions in Pattern Recognition. Proceedings of VIIth Conference of the International Association for the Development of Interdisciplinary Research(AIDRI), Geneva, 1997, 135-138.

[Ftani97b] F. Taniguchi, M. Kudo, M. Shimbo, Estimation of Class Regions in Feature Space Using Rough Set Theory. Proceedings of First International Conference on Conventional and Knowledge-Based Intelligent Electronic Systems(KES97), Aderade, 1997, 373 -377.

[atsu_97] A. Nakamura, An Efficient Exact Learning Algorithm for Ordered Binary Decision Diagrams. Proc. of ALT 1997, 307-322.

[atsu_96] A. Nakamura, Query Learning of Bounded-Width OBDDs. Proc. of ALT 1996, 37-50.

[atsu_95b] A. Nakamura, S. Miura, Learning Sparse Linear Combinations of Basis Functions over a Finite Domain. Proc. of ALT 1995, 138-150.

[atsu_95c] A. Nakamura, N. Abe, On-line Learning of Binary and n-ary Relations over Multi-dimensional Clusters. Proc. of COLT 1995, 214-221.

[nabe_95] N. Abe, H. Li, A. Nakamura, On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms. Proc. of ICML 1995, 3-11.

[atsu_94] A. Nakamura, N. Abe, J. Takeuchi, Efficient Distribution-free Population Learning of Simple Concepts. Proc. of AII/ALT 1994, 500-515.

[atsu_93] A. Nakamura, N. Abe, Exact Learning of Linear Combinations of Monotone Terms from Function Value Queries. Proc. fo ALT 1993, 300-313.

Technical Reports

[Mine98d] 工藤峰一, 溝井俊明, 新保 勝, 手書き漢字同定のための動的モデルによるストローク抽出. 電子情報通信学会技術報告, PRMU98-40(1998-06), 25-32.

[Masu98] 益山直人, 工藤峰一, 外山 淳, 新保 勝, 分枝限定法利用の最近隣法における終端条件の効果. 電子情報通信学会技術報告, PRMU98-41(1998-06), 33-37.

[Tenmo98c] 天元 宏, 工藤峰一, 新保 勝, 混合分布を用いた識別規則における最適な混合数の選択. 電子情報通信学会技術報告, PRMU98-42(1998-06), 39-43.

[Mine98g] 工藤峰一, 外山 淳, 新保 勝, 通過領域に着目した多次元空間における曲線分類. 電子情報通信学会技術報告, PRMU98-116(1998-06), 29-35.

[Haya_1998] 林 裕樹, 工藤峰一, 外山 淳, 新保 勝, “複数領域の隣接関係に基づく自然シーンの高速ラベリング.” 電子情報通信学会技術報告, IE98-18(1998-06), 17-24.

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