Graduate School of Information Science and Technology, Hokkaido University

Publications

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2021

Journal Papers

[Lu_2020a] Kejing Lu and Mineichi Kudo, “AdaLSH: Adaptive LSH for Solving c-approximate Maximum Inner Product Search Problem”. IEICE Trans. Information and System, E104-D-1(2021), 131-145.

Conference Papers

Technical Reports

2020

Journal Papers

Conference Papers

[Lu_2020b] Kejing Lu and Mineichi Kudo, “R2LSH: A Nearest Neighbor Search Scheme Based on Two-dimensional Projected Spaces”. ICDE 2020, 1045-1056.  DOI: 10.1109/ICDE48307.2020.00095

[Lu_2020c] Kejing Lu, Hongya Wang, Wei Wang and Mineichi Kudo, “VHP: Approximate Nearest Neighbor search via Virtual Hypersphere partitioning”. Proc. VLDB Endow 13(9), 1443-1455.  DOI: 10.14778/3397230.3397240

[Maekawa_2020] M Maekawa, A Nakamura and M Kudo, “Data-Dependent Conversion to a Compact Integer-Weighted Representation of a Weighted Voting Classifier.” Proc. of ACML, 2020, 241-256.

Technical Reports

2019

Journal Papers

[Sun_2019] L Sun and M Kudo, “Multi-label classification by polytree-augmented classifier chains with label-dependent features.” Pattern Analysis and Applications, 22-3(2019), 1029–1049. DOI:10.1007/s10044-018-0711-6

Conference Papers

[Tai_2019] Mariko Tai and Mineichi Kudo, “A Supervised Laplacian Eigenmaps Algotithm for Visualization of Multi-label Data: SLE-ML.” Proc. of CIARP, 2019, 525-534.

Technical Reports

[Maekawa_2019c]M Maekawa, A Nakamura, M Kudo, “Conversion from a Real-Weighted Majority Voting Classifier to a Small-Non-Negative-Integer-Weighted Thresholded Voting Classifier.” ACML 2019 Workshop on Statistics & Machine Learning Researchers in Japan, (2019), Nagoya.

[Todo_2019]K Todo, A Nakamura, M Kudo, “A Fast Approximate Algorithm for k-Median Problem on a Graph.” 15th International Workshop on Mining and Learning with Graphs, (2019), Anchorage, Alaska.

[Shirakawa_2019a]R Shirakawa, A Nakamura, M Kudo, “Learning a Nonlinear Model of Subgraph Features Using Monte Carlo Tree Search.” ACML 2019 Workshop on Statistics & Machine Learning Researchers in Japan, (2019), Nagoya.

2018

Journal Papers

[Sun_2018] L Sun and M Kudo, “Optimization of Classifier Chains via Conditional Likelihood Maximization.” Pattern Recognition. 74(2018), 503-517. DOI:10.1016/j.patcog.2017.09.034

[Watanabe_2018] Ryo Watanabe, Junpei Komiyama, Atsuyoshi Nakamura and Mineichi Kudo, “UCB-SC: A Fast Variant of KL-UCB-SC for Budgeted Multi-Armed Bandit Problem. IEICE Trans., 101-A(3)(2018), 662-667 (2018). doi.org/10.1587/transfun.E101.A.662

2017

Journal Papers

[Sun_2017b] L Sun and M Kudo, “Optimization of Classifier Chains via Conditional Likelihood Maximization.” Pattern Recognition. to appear.

[Backhus_2017] Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo and Masanori Sugimoto: An Online Self-Constructive Normalized Gaussian Network with Localized Forgetting.IEICE Transactions 100-A(3): 865-876 (2017).

[Tabata_2017] Koji Tabata, Atsuyoshi Nakamura and Mineichi Kudo:
An Efficient Approximate Algorithm for the 1-Median Problem on a Graph.IEICE Transactions 100-D(5): 994-1002 (2017).

[Sun_2017a] L Sun, M Kudo and K Kimura, “READER: Robust Semi-Supervised Multi-Label Dimension Reduction”, IEICE, to appear.

[Watanabe_2017] Ryo Watanabe, Junpei Komiyama, Atsuyoshi Nakamura and Mineichi Kudo, “KL-UCB-Based Policy for Budgeted Multi-armed Bandits with Stochastic Action Costs.” IEICE to appear

Conference Papers

Technical Reports

[Hanada_2017] Hiroyuki Hanada, Mineichi Kudo, Atsuyoshi Nakamura:
On Practical Accuracy of Edit Distance Approximation Algorithms.CoRR abs/1701.06134 (2017)

[Kimura_2017] Keigo Kimura, Lu Sun, Mineichi Kudo:
MLC Toolbox: A MATLAB/OCTAVE Library for Multi-Label Classification.CoRR abs/1704.02592 (2017)

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 (accepted for publication). DOI:10.1016/j.specom.2016.07.001

[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_2016] 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.

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.

[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.

[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.

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. (in press)

[Lu_2015] Guoliang Lu, Yiqi Zhou, Xueyong Li, Mineichi Kudo. Efficient action recognition via local position offset of 3D skeletal body joints, Multimedia Tools and Applications. DOI:10.1007/s11042-015-2448-1

[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

[Atsu_2015a] Atsuyoshi Nakamura, Ichigaku Takigawa, Hisashi Tosaka, Mineichi Kudo and Hiroshi Mamitsuka, “Mining approximate patterns with frequent locally optimal occurrences”, Discrete Applied Mathematics. (in press)

[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. (accepted for publication)

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), 2015. (to appear)

[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.

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. to apper 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

[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.

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)

[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

[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_2012] 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_2011] 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.

[LU_2012] G. LU, M. KUDO and J. TOYAMA, Action Recognition via Sparse Representation of Characteristic Frames. Proceeding of the 21st International Conference on Pattern Recognition, to appear, 2012.

[Tao_2012] 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, pp. 1759-1762, 2012.

Technical Reports

2011

Journal Papers

[Tetsuji_2011] T. Takahasi, M. Kudo and A. Nakamura, Construction of Convex Hull Classifiers in High Dimensions. Pattern Recognition Letters , in press. DOI:10.1016/j.patrec.2011.06.020

[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_2011] 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

[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_2011] 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_2011] 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_2011] 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_2011] 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_2011] S. Tao, M. Kudo, H. Nonaka, 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, pp. 119-127, 2012.

[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_2011] 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

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

[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 Computer Science, vol. 6331, Marcus Hutter et al. (eds.), Springer, 2010, 375–389.DOI:10.1007/978-3-642-16108-7

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.

[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.

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. 14th Iberoamerican Congress on Pattern Recognition – CIARP 2009, Guadalajara, Mexico, 2009, pp. 441–448.

Technical Reports

[uchiya_2009a] T. Uchiya, A. Nakamura and M. Kudo, Adversarial Bandit Problems with Multiple Plays. The IEICE Technical Report, COMP2009-25–COMP2009-31(2009), Vol.109, No. 195, pp. 13-20.(In Japanese)

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.

[taisuke_08] T. Hosokawa, M. Kudo, H. Nonaka and J. Toyama, Soft Authentication Using an Infrared Ceiling Sensor Network. Pattern Analysis and Applications, accepted for publication.

[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, accepted for publication.

[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.

[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.

[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 19th International Conference on Pattern Recognition (ICPR2008), tampa, .

[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.

Technical Reports

[kanda_2008a] Y. Kanda and M. Kudo, Aggregation of Web Search Results using Overlapping Hierarchical Clustering. The IEICE Technical Report, PRMU 2008-184, 219-224. (In Japanese)

[uchiya_2008a] T. Uchiya, A. Nakamura and M. Kudo, On effect of balancing investment in nonstochastic multi-armed bandit problems. The IEICE Technical Report, PRMU 2008-183, 213-218. (In Japanese)

[takahashi_2008a] T. Takahashi, M. Kudo and A. Nakamura, Classifier Selection in a Family of Polyhedron Classifiers. The IEICE Technical Report, PRMU 2008-153, 37-42. (In Japanese)

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.

[mine_07a] M. Kudo and T. Murai, Extended DNF Expression and Variable Granularity in Information Tables. IEEE Trans. on Fuzzy Sets and Systems, to appear in 2007

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] S. Hayashi, M. Kudo, Utilization of Pen-Input Interface for Handwritten Documents. Proceedings of the Human Interface Symposium2007, 2007. 313-318. (In Japanese)

Technical Reports

[tosaka_2007a] Hisashi Tosaka, Atsuyoshi Nakamura and Mineichi Kudo, Finding of Frequent Similar Subtrees in Tree-Structured Data. The IEICE Technical Report, PRMU 2007-28(2007), 7-12. (In Japanese)

[kamiya_2007a] K. Kamiya, M. Kudo, H. Nonaka and J. Toyama, A Study on Sitting-Posture Analysis by Pressure Sensors. The IEICE Technical Report, UBI 2007-74(2007), 41-46.

[satoshi_2007a] Satoshi Shirai and Mineichi Kudo, Classifier Fusion on the Basis of Data Selection and Feature Selection. The IEICE Technical Report, PRMU 2007-39(2007), 69-74. (In Japanese)

[maico_2007a] Maiko Sato, Mineichi Kudo and Jun Toyama, Analysis of Volume Prototypes and Comparison with Mixture Models. The IEICE Technical Report, PRMU 2007-43(2007), 93-98. (In Japanese)

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), to appear.

[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.

Technical Reports

[mine_2006a] Mineichi Kudo, Feature Selection – So far and from now on -. The IEICE Technical Report, PRMU 2006-167(2006), 37–42. (In Japanese)

[muto_2006b] Yuji Muto and Mineichi Kudo, An Attribute Value Abstraction Using Granularity. The IEICE Technical Report, PRMU 2006-174(2006), 19–24. (In Japanese)

[tabata_2006a] Kenji Tabata and Mineichi Kudo, Information Compression by Volume Prototypes. The IEICE Technical Report, PRMU 2006-175(2006), 25–30. (In Japanese)

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] T. Kawata, M, Kudo, A. Nakamura and J. Toyama, Detection of Wrong Characters by Probability Transitional Patterns of Two-Directional N-gram Probabilities. The IEICE Transactions on Information and Systems, J88-D-II-3(2005), 629–635. (In Japanese)

Conference Papers

[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.

[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.

[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.

[hase_2005a] H. Hasegawa, M, Kudo and A. Nakamura, Creation of Better Patterns Set for Reputation Extraction Using Both Structural and Content Information. Proc. of Data Engineering Workshop 2005, 5C-o4.

[ino_2005a] Comparison of Methods Finding a Community Subclass. 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.

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

[hase_2005b] H. Hasegawa, M. Kudo, A. Nakamura, Empirical Study on Usefulness of Algorithm SACwRApper for Reputation Extraction from the WWW. Proc. of KES (4), 2005, 668-674.

Technical Reports

[atsu_2005b] A. Nakamura, On Online Learning of Linear Ranking Functions, In: Proceedings of 2005 Winter Workshop on Theoretical Computer Science and its Applications, Kokyuroku 1426, Research Institute for Mathematical Sciences, Kyoto University, pp. 51-56, 2005. (In Japanese)

[atsu_2005c] A. Nakamura, How Easy to Learn Linear Ranking Functions. IEICE Technical Report, 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.

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

[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.

[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.

[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.

[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.

[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.

[atsu_2004a] A. Nakamura and M.Kudo, Web Graph Partitioning by Community Topology. In: Proceedings of the 4th Data Mining Workshop, pp 57-64, 2005. (In Japanese)

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

[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.

Technical Reports

[atsu_2004b] A. Nakamura and M. kudo, Mining Frequent Trees with Nnode-Inclusion Constraints. IEICE Technical Report, 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] K. Aoki, T. Watanabe and M. Kudo, Design of Decision Trees Using Class-Dependent Feature Subsets. The IEICE Transactions on Information and Systems, J86-D-II-8(2003), 1156–1165. (In Japanese)

[Yasu03b] Y. Mori and M. Kudo, Interactive Data Analysis Based on Graph and Construction of Decision Trees. The IEICE Transactions on Information and Systems, J86-D-II-8(2003), 1166-1176. (In Japanese)

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] T. Kawata, A. Nakamura, J. Toyama and M. Kudo, Detection of Wrong Character Using Probability Transitional Patterns of Both-Direction N-gram Probabilities. The IEICE Technical Report, PRMU 2003-75(2003), 1–5. (In Japanese)

[Shidara03] Y. Shidara, A. Nakamura and M. Kudo, Relationship between Rule Accuracy and a Degree of Interest. The IEICE Technical Report, PRMU 2003-76(2003), 7–11. (In Japanese)

[Mine03b] M. Kudo, Acceleration of the k-nearest neighbor method using inclusion and exclusion. The IEICE Technical Report, PRMU 2003-09(2003), 91–95. (In Japanese)

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

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

[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.

[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.

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] N. Masuyama, M. Kudo, J. Toyama and M. Shimbo, Acceleration of the k-Nearest Neighbor Algorithm by Addition of Termination Conditions in Pattern Recognition Problems. The IEICE Transactions on Information and Systems, J84-D-II-3(2001), 439–447. (In Japanese)

Conference Papers

[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.

[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.

[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.

[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.

-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] M. Kudo, T. Mizoi and M. Shimbo, Stroke Extraction Using Dynamic Modeling for Identifying Handwritten Chinese Characters. The IEICE Technical Report, PRMU98-40(1998-06), 25-32. (In Japanese)

[Masu98] N. Masuyama, M. Kudo, J. Toyama and M. Shimbo, Effectiveness of the Addition of a Termination Condition to the Branch and Bound Based Nearest Neighbor Method. The IEICE Techical Report, PRMU98-41(1998-06), 33-37. (In Japanese)

[Tenmo98c] H. Tenmoto, M. Kudo and M. Shimbo, Optimal selection of the number of components in classifiers based on mixture models. The IEICE Technical Report, PRMU98-42(1998-06), 39-43. (In Japanese)

[Mine98g] M. Kudo, J. Toyama and M. Shimbo, Classification of Curves in a Multidimensional Space on the Basis of Passing-Through Regions. The IEICE Technical Report, PRMU98-116(1998-06), 29-35. (In Japanese)

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