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List of Papers
Einoshin Suzuki Last modified date：2024.04.24

Professor / Intelligent Science Division / Department of Informatics / Faculty of Information Science and Electrical Engineering


Papers
1. Qiming Zou, Einoshin Suzuki: , Compact Goal Representation Learning via Information Bottleneck in Goal-Conditioned Reinforcement Learning, IEEE Transactions on Neural Networks and Learning Systems, 10.1109/TNNLS.2023.3344880, (accepted for publication).
2. Ryosuke Miyake, Tetsu Matsukawa, Einoshin Suzuki, Image Generation from Hyper Scene Graphs with Multiple Types of Trinomial Hyperedges, SN Computer Science, (accepted for publication).
3. Liheng Shen, Tetsu Matsukawa, Einoshin Suzuki, SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection, Advances in Knowledge Discovery and Data Mining, Part ?. Lecture Notes in Computer Science ?, Springer (PAKDD 2024), (accepted for publication), 2024.05.
4. Ryosuke Miyake, Tetsu Matsukawa, Einoshin Suzuki, Image Generation from Hyper Scene Graphs with Trinomial Hyperedges Using Object Attention, Proc. Nineteenth International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024), Vol. 2: VISAPP (Nineteenth International Conference on Computer Vision Theory and Applications), 10.5220/0012472500003660. , 266-279, 2024.02.
5. Wenbo Li, Yao Yang, Einoshin Suzuki, Class-Specific Word Sense Aware Topic Modeling via Soft Orthogonalized Topics, Proc. 32nd International Conference on Information and Knowledge Management (CIKM 2023), 10.1145/3583780.3614809 , 1218-1227, 2023.10.
6. Jose Alejandro Avellaneda Gonzalez, Tetsu Matsukawa, Einoshin Suzuki, Cross-Modal Self-Supervised Feature Extraction for Anomaly Detection in Human Monitoring, Proc. Nineteenth International Conference on Automation Science and Engineering (CASE 2023), 10.1109/CASE56687.2023.10260493 , 2023.08.
7. Kang Zhang, Einoshin Suzuki, Judging Credible and Unethical Statistical Data Explanations via Phrase Similarity Graph, Proc. 2023 Pacific Asia Conference on Information Systems (PACIS 2023), paper 121, 2023.07.
8. Qiming Zou, Einoshin Suzuki, Sample-Efficient Goal-Conditioned Reinforcement via Predictive Information Bottleneck for Goal Representation Learning, Proc. 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), 10.1109/ICRA48891.2023.10161213, 9523-9529, 2023.05.
9. Ryosuke Miyake, Tetsu Matsukawa, Einoshin Suzuki, Image Generation from Hyper Scene Graphs with Trinomial Hyperedges, Proc. Eighteenth International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023), Vol. 5: VISAPP (Eighteenth International Conference on Computer Vision Theory and Applications), 10.5220/0011699300003417, 185-195, 2023.02.
10. Kang Zhang, Muhammad Fikko Fadjrimiratno, Einoshin Suzuki, Anomaly Detection via Spatial and Semantic Attributed Graph in Human Monitoring, Sensors, 10.3390/s23031307, 23, 3, Article 1307, 2023.02.
11. Liheng Shen, Tetsu Matsukawa, Einoshin Suzuki, Detecting Video Anomalous Events with an Enhanced Abnormality Score, PRICAI 2022: Trends in Artificial Intelligence, Part I, LNCS 13629, 10.1007/978-3-031-20862-1_15, 202-217 , 2022.11.
12. Ning Dong, Einoshin Suzuki, GIAD-ST: Detecting Anomalies in Human Monitoring Based on Generative Inpainting via Self-Supervised Multi-Task Learning, Journal of Intelligent Information Systems, 10.1007/s10844-022-00722-8, 59, 3, 733-754, 2022.12.
13. Kang Zhang, Hiroaki Shinden, Tatsuki Mutsuro, Einoshin Suzuki, Judging Instinct Exploitation in Statistical Data Explanations Based on Word Embedding, Proc. Fifth AAAI/ACM Conference on AI, Ethics, and Society (AIES 2022), 10.1145/3514094.3534171, 867-879, 2022.08.
14. Kang Zhang, Muhammad Fikko Fadjrimiratno, Einoshin Suzuki, Context-Based Anomaly Detection via Spatial Attributed Graphs in Human Monitoring, Proc. 28th International Conference on Neural Information Processing (ICONIP-2021), Part I, 10.1007/978-3-030-92185-9_37, 450-463, 2021.12.
15. Qiming Zou, Einoshin Suzuki, Contrastive Goal Grouping for Policy Generalization in Goal-Conditioned Reinforcement Learning, Proc. 28th International Conference on Neural Information Processing (ICONIP-2021), Part I, 10.1007/978-3-030-92185-9_20., 240-253, 2021.12.
16. Ning Dong, Einoshin Suzuki, GIAD: Generative-Inpainting-Based Anomaly Detection from Salient Regions in Human Monitoring, Proc. PRICAI 2021: Trends in Artificial Intelligence - 18th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2021), Part II, LNCS 13032, 10.1007/978-3-030-89363-7_32, 418-432, 2021.11.
17. Mirai Takayanagi, Yasuo Tabei, Einoshin Suzuki, Hiroto Saigo, Sparse nonnegative interaction models, IEEE Access, 10.1109/ACCESS.2021.3099473, 9, 109994-110005, 2021.07.
18. Wenbo Li, Tetsu Matsukawa, Hiroto Saigo, Einoshin Suzuki, Topic Modeling for Sequential Documents Based on Hybrid Inter-Document Topic Dependency, Journal of Intelligent Information Systems, 10.1007/s10844-020-00635-4, 56, 3, 435-458, 2021.07.
19. Wenbo Li, Einoshin Suzuki, Adaptive and Hybrid Context-Aware Fine-Grained Word Sense Disambiguation in Topic Modeling Based Document Representation, Information Processing & Management, 10.1016/j.ipm.2021.102592, 58, 4, Article 102592, 2021.06.
20. Yusuke Ohtsubo, Tetsu Matsukawa, Einoshin Suzuki, Semi-Supervised Few-Shot Classification with Deep Invertible Hybrid Models, CoRR abs/2105.10644, (arXiv:2105.10644), 10.48550/arXiv.2105.10644, 2021.05.
21. Muhammad Fikko Fadjrimiratno, Yusuke Hatae, Tetsu Matsukawa, Einoshin Suzuki, Detecting Anomalies from Human Activities by an Autonomous Mobile Robot Based on "Fast and Slow" Thinking, Proc. Sixteenth International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021), Vol. 5: VISAPP (Sixteenth International Conference on Computer Vision Theory and Applications), 10.5220/001031350943095, 943-953, 2021.02.
22. Tetsu Matsukawa, Einoshin Suzuki, Convolutional Feature Transfer via Camera-Specific Discriminative Pooling for Person Re-Identification, Proc. 25th International Conference on Pattern Recognition (ICPR 2020), 10.1109/ICPR48806.2021.9412420, 8408-8415, 2021.01.
23. Kaikai Zhao, Takashi Imaseki, Hiroshi Mouri, Einoshin Suzuki, Tetsu Matsukawa, From Certain to Uncertain: Toward Optimal Solution for Offline Multiple Object Tracking, Proc. 25th International Conference on Pattern Recognition (ICPR 2020), 10.1109/ICPR48806.2021.9413215, 2506-2513, 2021.01.
24. Wenbo Li, Einoshin Suzuki, Hybrid Context-Aware Word Sense Disambiguation in Topic Modeling based Document Representation, Proc. 2020 IEEE International Conference on Data Mining (ICDM 2020), 10.1109/ICDM50108.2020.00042 , 332-341, 2020.11.
25. Ning Dong, Yusuke Hatae, Muhammad Fikko Fadjrimiratno, Tetsu Matsukawa, Einoshin Suzuki, Experimental Evaluation of GAN-Based One-Classs Anomaly Detection on Office Monitoring, Foundations of Intelligent Systems, LNAI 12117 (ISMIS 2020), 10.1007/978-3-030-59491-6_20 , 214-224, 2020.09.
26. Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, Yoichi Sato, Hierarchical Gaussian Descriptor with Application to Person Re-Identification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 10.1109/TPAMI.2019.2914686, 2020.09.
27. Hirofumi Fujita, Tetsu Matsukawa, Einoshin Suzuki, Detecting Outliers with One-Class Selective Transfer Machine, Knowledge and Information Systems, An International Journal, 10.1007/s10115-019-01407-5 , 62, 5, 1781-1818, 2020.05.
28. Wenbo Li, Tetsu Matsukawa, Hiroto Saigo, Einoshin Suzuki, Context-Aware Latent Dirichlet Allocation for Topic Segmentation, Advances in Knowledge Discovery and Data Mining (PAKDD 2020), Part I, Lecture Notes in Computer Science 12084,, 10.1007/978-3-030-47426-3_37, 475-486, 2020.05.
29. Yusuke Hatae, Qingpu Yang, Muhammad Fikko Fadjrimiratno, Yuanyuan Li, Tetsu Matsukawa, Einoshin Suzuki, Detecting Anomalous Regions from an Image Based on Deep Captioning, Proc. Fifteenth International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020), Vol. 5: VISAPP (Fifteenth International Conference on Computer Vision Theory and Applications), 10.5220/0008949603260335, 326-335, (Best Poster Award), 2020.02.
30. Yusuke Ohtsubo, Tetsu Matsukawa, Einoshin Suzuki, Harnessing GAN with Metric Learning for One-Shot Generation on a Fine-Grained Category, Proc. 31st International Conference on Tools with Artificial Intelligence (ICTAI 2019), 10.1109/ICTAI.2019.00126, 891-898, 2019.11.
31. Kaikai Zhao, Tetsu Matsukawa, Einoshin Suzuki, Experimental Validation for N-ary Error Correcting Output Codes for Ensemble Learning of Deep Neural Networks, Journal of Intelligent Information Systems, 10.1007/s10844-018-0516-5 , 52, 2, 367-392, 2019.04.
32. Tetsu Matsukawa, Einoshin Suzuki, Kernelized Cross-View Quadratic Discriminant Analysis for Person Re-Identification, Proc. Sixteenth International Conference on Machine Vision Applications (MVA 2019), ?, paper 04-06, 2019.05.
33. Kaikai Zhao, Tetsu Matsukawa, Einoshin Suzuki, Retraining: A Simple Way to Improve the Ensemble Accuracy of Deep Neural Networks for Image Classification, Proc. 25th International Conference on Pattern Recognition (ICPR 2018), 10.1109/ICPR.2018.8545535 , 860-867, 2018.08.
34. Soichiro Oura, Tetsu Matsukawa, Einoshin Suzuki, Multimodal Deep Neural Network with Image Sequence Features for Video Captioning, Proc. 2018 International Joint Conference on Neural Networks (IJCNN 2018), 10.1109/IJCNN.2018.8489668 , 3296-3302, 2018.07.
35. Einoshin Suzuki, Exploiting Micro-Clusters to Close The Loop in Data-Mining Robots for Human Monitoring, Proc. Symposium on Integrating Representation, Reasoning, Learning, and Execution for Goal Directed Autonomy (SIRLE 2018), 2018 AAAI Spring Symposium Series, Technical Report SS-18, AAAI Press, ?, 595-597, 2018.03.
36. Hirofumi Fujita, Tetsu Matsukawa, Einoshin Suzuki, One-Class Selective Transfer Machine for Personalized Anomalous Facial Expression Detection
, Proc. Thirteenth International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018), Vol. 5: VISAPP (Thirteenth International Conference on Computer Vision Theory and Applications), 10.5220/0006613502740283, 274-283, 2018.01.
37. Yutaka Deguchi, Daisuke Takayama, Shigeru Takano, Vasile-Marian Scuturici, Jean-Marc Petit, Einoshin Suzuki, Skeleton Clustering by Multi-Robot Monitoring for Fall Risk Discovery, Journal of Intelligent Information Systems, 10.1007/s10844-015-0392-1, 48, 1, 75-115, 2017.01.
38. Matsukawa Tetsu, Einoshin Suzuki, Person Re-Identification Using CNN Features Learned from Combination of Attributes, Proc. 23rd International Conference on Pattern Recognition (ICPR 2016), ?, 2429-2434, 2016.12.
39. Matsukawa Tetsu, Takahiro Okabe, Einoshin Suzuki, Yoichi Sato, Hierarchical Gaussian Descriptor for Person Re-Identification, Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), ?, 1363-1372, 2016.06.
40. Shin Ando, Einoshin Suzuki, Minimizing Response Time in Time Series Classification , Knowledge and Information Systems, An International Journal, 10.1007/s10115-015-0826-7, 46, 2, 449-476, 2016.02.
41. Yutaka Deguchi, Einoshin Suzuki, Hidden Fatigue Detection for a Desk Worker Using Clustering of Successive Tasks, Ambient Intelligence (AmI 2015), LNCS 9425, 10.1007/978-3-319-26005-1_18, 263-283, 2015.11.
42. Kaikai Zhao, Einoshin Suzuki, Clustering Classifiers Learnt from Local Datasets Based on Cosine Similarity, Foundations of Intelligent Systems, LNCS 9384 (ISMIS 2015), 10.1007/978-3-319-25252-0_16 , 2015.10.
43. Einoshin Suzuki, On the Feasibility of Discovering Meta-Patterns from a Data Ensemble, Discovery Science (DS 2015), LNAI 9356, 10.1007/978-3-319-24282-8_22, 266-274, 2015.10.
44. Vasile-Marian Scuturici, Yann Gripay, Jean-Marc Petit, Yutaka Deguchi, Einoshin Suzuki, Continuous Query Processing over Data, Streams and Services: Application to Robotics, New Trends in Databases and Information Systems (ADBIS 2015), CCIS 539, 10.1007/978-3-319-23201-0_5, 36-43, 2015.09.
45. Somar Boubou, A. H. Abdul Hafez, Einoshin Suzuki, Visual Impression Localization of Autonomous Robots, Proc. 2015 IEEE International Conference on Automation Science and Engineering (CASE 2015), 10.1007/s10844-014-0329-0, 328-334, 2015.08.
46. Einoshin Suzuki, Yutaka Deguchi, Matsukawa Tetsu, Shin Ando, Hiroaki Ogata, Masanori Sugimoto, Toward a Platform for Collecting, Mining, and Utilizing Behavior Data for Detecting Students with Depression Risks, Proc. Eighth International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2015), 10.1145/2769493.2769538 , 2015.07.
47. Somar Boubou, Einoshin Suzuki, Classifying Actions Based on Histogram of Oriented Velocity Vectors, Journal of Intelligent Information Systems, 10.1007/s10844-014-0329-0, 44, 1, 49-65, 2015.01.
48. Shin Ando, Theerasak Thanomphongphan, Daisuke Hoshino, Yoichi Seki, Einoshin Suzuki, Ensemble Anomaly Detection from Multi-resolution Trajectory Features, Data Mining and Knowledge Discovery, 10.1007/s10618-013-0334-x, 29, 1, 39-83, 2015.01.
49. Shin Ando, Einoshin Suzuki, Discriminative Learning on Exemplary Patterns of Sequential Numerical Data, Proc. 2014 IEEE International Conference on Data Mining (ICDM 2014), 10.1109/ICDM.2014.122, 1-10, 2014.12.
50. Ryosuke Kondo, Yutaka Deguchi, Einoshin Suzuki, Developing a Face Monitoring Robot for a Desk Worker, Ambient Intelligence (AmI 2014), LNCS 8850, 10.1007/978-3-319-14112-1_19, 226-241, 2014.11.
51. Daisuke Takayama, Yutaka Deguchi, Shigeru Takano, Vasile-Marian Scuturici, Jean-Marc Petit, Einoshin Suzuki, Multi-view Onboard Clustering of Skeleton Data for Fall Risk Discovery, Ambient Intelligence (AmI 2014), LNCS 8850, 10.1007/978-3-319-14112-1_21, 258-273, 2014.11.
52. Bin Tong, Tetsuro Morimura, Einoshin Suzuki, Tsuyoshi Ide, Probabilistic Two-Level Anomaly Detection for Correlated Systems, Proc. 21st European Conference on Artificial Intelligence (ECAI 2014), 10.3233/978-1-61499-419-0-1109, 1109-1110, 2014.08.
53. Angdy Erna, Linli Yu, Kaikai Zhao, Wei Chen, Einoshin Suzuki, Facial Expression Data Constructed with Kinect and their Clustering Stability, Active Media Technology, Lecture Notes in Computer Science 8610 (AMT 2014), 10.1007/978-3-319-09912-5_35, 421-431, 2014.08.
54. Daisuke Ikeda, Einoshin Suzuki, Finding Peculiar Compositions of Two Frequent Strings with Background Texts , Knowledge and Information Systems, An International Journal, 10.1007/s10115-013-0688-9, 41, 2, 499-530, 2014.02.
55. Yutaka Deguchi, Einoshin Suzuki, Skeleton Clustering by Autonomous Mobile Robots for Subtle Fall Risk Discovery, Foundations of Intelligent Systems, Lecture Notes in Computer Science 8502 (ISMIS 2014), 10.1007/978-3-319-08326-1_51, 500-505, 2014.06.
56. Yutaka Deguchi, Daisuke Takayama, Shigeru Takano, Vasile-Marian Scuturici, Jean-Marc Petit, Einoshin Suzuki, Multiple-Robot Monitoring System Based on a Service-Oriented DBMS, Proc. Seventh International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2014), 10.1145/2674396.2674418, 2014.05.
57. Bin Tong, Junbin Gao, Thach Nguyen Huy, Hao Shao, Einoshin Suzuki, Transfer Dimensionality Reduction by Gaussian Process in Parallel, Knowledge and Information Systems, An International Journal, 10.1007/s10115-012-0601-y, 38, 3, 567-597, 2014.03.
58. Thach Nguyen Huy, Bin Tong, Hao Shao, Einoshin Suzuki, Transfer Learning by Centroid Pivoted Mapping in Noisy Environment, Journal of Intelligent Information Systems, 41, 1, 39-60, 2013.08.
59. Shigeru Takano, Ilya Loshchilov, David Meunier, Michele Sebag, Einoshin Suzuki, Fast Adaptive Object Detection towards a Smart Environment by Mobile Robots, Ambient Intelligence (AmI 2013), LNCS , 8309, 182-197, 2013.12.
60. Thach Nguyen Huy, Hao Shao, Bin Tong, Einoshin Suzuki, A Feature-Free and Parameter-Light Multi-Task Clustering Framework, Knowledge and Information Systems, An International Journal, 36, 1, 251-276, 2013.07.
61. Shin Ando, Einoshin Suzuki, Time-sensitive Classification of Behavioral Data, Proc. Thireenth SIAM International Conference on Data Mining (SDM 2013), 458-466, 2013.05.
62. Hao Shao, Bin Tong, Einoshin Suzuki, Extended MDL Principle for Feature-based Inductive Transfer Learning, Knowledge and Information Systems, An International Journal, 35, 2, 365-389, 2013.05.
63. Einoshin Suzuki, Special Issue on Discovery Science: Guest Editor's Introduction, The Computer Journal, 56, 3, 271-273, 2013.03.
64. CHOU BIN-HUI, Einoshin Suzuki, Detecting Academic Plagiarism with Graphs, Extraction et Gestion des Connaissances (EGC'2013), 293-304, 2013.01.
65. CHOU BIN-HUI, Einoshin Suzuki, RoClust: Role Discovery for Graph Clustering, Web Intelligence and Agent Systems, An International Journal, 11, 1, 1-20, 2013.01.
66. Shigeru Takano, Ilya Loshchilov, David Meunier, Michele Sebag, Einoshin Suzuki, Fast Adaptive Object Detection for a Mobile Robot, Proc. 2013 International Symposium on Information Science and Electrical Engineering (ISEE 2013), 11-11, 2013.01.
67. Emi Matsumoto, Einoshin Suzuki, Similarity Measure between a Pair of Kinect Skeletons for Data Stream Clustering, Proc. 2013 International Symposium on Information Science and Electrical Engineering (ISEE 2013), 7-7, 2013.01.
68. Hao Shao, Bin Tong, Einoshin Suzuki, Query by Committee in a Heterogeneous Environment, Advanced Data Mining and Applications (ADMA 2012), 7713, 186-198, 2012.12.
69. Asuki Kouno, Daisuke Takayama, Einoshin Suzuki, Predicting the State of a Person by an Office-Use Autonomous Mobile Robot, Proc. 2012 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2012), 80-84, 2012.12.
70. Einoshin Suzuki, Emi Matsumoto, Asuki Kouno, Data Squashing for HSV Subimages by an Autonomous Mobile Robot, Discovery Science (DS 2012), LNAI, 7569, 95-109, 2012.10.
71. Shinsuke Sugaya, Daisuke Takayama, Asuki Kouno, Einoshin Suzuki, Intelligent Data Analysis by a Home-Use Human Monitoring Robot, Advances in Intelligent Data Analysis XI - 11th International Symposium (IDA 2012), LNCS, 7619, 381-391, 2012.10.
72. Bin Tong, Hao Shao, CHOU BIN-HUI, Einoshin Suzuki, Linear Semi-supervised Projection Clustering by Transferred Centroid Regularization, Journal of Intelligent Information Systems, 39, 2, 461-490, 2013.10.
73. David Meunier, Yutaka Deguchi, Riad Akrour, Einoshin Suzuki, Marc Schoenauer, Michele Sebag, Direct Value Learning: a Preference-Based Approach to Reinforcement Learning, Proc. ECAI-12 Workshop on Preference Learning: Problems and Applications in AI (PL-12), 42-47, 2012.08.
74. Bin Tong, Weifeng Jia, Yanli Ji, and Einoshin Suzuki, Linear Semi-supervised Dimensionality Reduction with Pairwise Constraint for Multiple Subclasses, IEICE Transactions on Information and Systems, E95-D, 3, 812-820, 2012.03.
75. Kouhei Takemoto, Shigeru Takano, and Einoshin Suzuki, Human Detection by a Small Autonomous Mobile Robot, Extraction et Gestion des Connaissances (EGC'2012), 531-536, 2012.02.
76. Shin Ando and Einoshin Suzuki, Role-behavior Analysis from Trajectory Data by Cross-domain Learning, Proc. Eleventh IEEE International Conference on Data Mining (ICDM 2011), 21-30, 2011.12.
77. Shigeru Takano and Einoshin Suzuki, New Object Detection for On-board Robot Vision by Lifting Complex Wavelet Transforms, Proc. Eleventh IEEE International Conference on Data Mining Workshops (ICDMW 2011), 911-916, 2011.12.
78. Somar Boubou, Asuki Kouno, and Einoshin Suzuki, Implementing Camshift on a Mobile Robot for Person Tracking and Pursuit, Proc. Eleventh IEEE International Conference on Data Mining Workshops (ICDMW 2011), 682-688, 2011.12.
79. Emi Matsumoto, Michele Sebag, and Einoshin Suzuki, SVM-based Human Avoidance for a Small Autonomous Mobile Robot, Proc. IRIT - Kyushu University Workshop on Data Mining and Media Processing, PS-2, 2011.11.
80. Shigeru Takano and Einoshin Suzuki, New Object Detection for On-board Robot Vision, Proc. IRIT - Kyushu University Workshop on Data Mining and Media Processing, OS2-1, 2011.11.
81. Bin Tong and Einoshin Suzuki, Dimensionality Reduction in Semi-supervised Learning and Transfer Learning, Proc. IRIT - Kyushu University Workshop on Data Mining and Media Processing, OS1-4, 2011.11.
82. Hiroshi Hirai, Bin-Hui Chou, Einoshin Suzuki, A Parameter-free Method for Discovering Generalized Clusters in a Network, Discovery Science (DS 2011), LNAI 6926, 135-149, 2011.10.
83. Emi Matsumoto, Michele Sebag, Einoshin Suzuki, Using SVM to Avoid Humans: A Case of a Small Autonomous Mobile Robot in an Office, Computer and Information Sciences II: 26th International Symposium on Computer and Information Sciences (ISCIS 2011) , 283-287, 2011.09.
84. Shao Hao, Bin Tong, Einoshin Suzuki, Compact Coding for Hyperplane Classifiers in Heterogeneous Environment, Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2011), Part III, LNCS 6913, 207-222, 2011.09.
85. Asuki Kouno, Jean-Marc Montanier, Shigeru Takano, Nicolas Bredeche, Marc Schoenauer, Michele Sebag, and Einoshin Suzuki, On-board Evolutionary Algorithm and Off-line Rule Discovery for Column Formation in Swarm Robotics, Proc. 2011 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2011), 220-227, 2011.08.
86. Nguyen Huy Thach, Shao Hao, Bin Tong, Einoshin Suzuki, A Compression-based Dissimilarity Measure for Multi-task Clustering, Foundations of Intelligent Systems, LNAI 6804 (ISMIS 2011), 123-132, 2011.06.
87. Shin Ando, Theerasak Thanomphongphan, Daisuke Hoshino, Yoichi Seki, and Einoshin Suzuki, ACE: Anomaly Clustering Ensemble for Multi-perspective Anomaly Detection, Proc. Eleventh SIAM International Conference on Data Mining (SDM 2011), 1-12, 2011.04.
88. Shao Hao and Einoshin Suzuki, Feature-based Inductive Transfer Learning through Minimum Encoding, Proc. Eleventh SIAM International Conference on Data Mining (SDM 2011), 259-270, 2011.04.
89. Bin Tong, Junbin Gao, Nguyen Huy Thach, and Einoshin Suzuki, Gaussian Process for Dimensionality Reduction in Transfer Learning, Proc. Eleventh SIAM International Conference on Data Mining (SDM 2011), 783-794, 2011.04.
90. Bin-Hui Chou and Einoshin Suzuki, Role Discovery for Graph Clustering, Web Technologies and Applications (APWeb), LNCS 6612 , 17-28, 2011.04.
91. Hiroshi Hirai, Shigeru Takano, Einoshin Suzuki, Simulating Swarm Robots for a Collision Avoidance Problem based on a Dynamic Bayesian Network, Proc. Tenth European Conference on Artificial Life (ECAL 2009), Part II, LNCS 5778, 416-423, 2011.12.
92. Einoshin Suzuki, Novel Statistical Rule Discovery for Understanding Behaviours of Swarm Robots, Proc. Fifth International Meeting on Statistical Implicative Analisys (A.S.I. 5), 452-454, 2010.11.
93. Bin Tong, ZhiGuang Qin, and Einoshin Suzuki, Topology Preserving SOM with Transductive Confidence Machine, Discovery Science, Lecture Notes in Artificial Intelligence (DS), LNAI 6332, 27-41, 2010.10.
94. Bin Tong, Shao Hao, Bin-Hui Chou, and Einoshin Suzuki, Semi-Supervised Projection Clustering with Transferred Centroid Regularization, Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), Part III, LNCS 6323, 306-321, 2010.09.
95. Asuki Kouno, Shigeru Takano, Einoshin Suzuki, Constructing Low-cost Swarm Robots that March in Column Formation, Swarm Intelligence (ANTS), LNCS 6234, 556-557, 2010.09.
96. Swagat Kumar, Nguyen Huy Thach, and Einoshin Suzuki, Understanding the Behaviour of Reactive Robots in a Patrol Task by Analysing their Trajectories, Proc. 2010 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), 56-63, 2010.09.
97. Bin-Hui Chou and Einoshin Suzuki, Discovering Community-Oriented Roles of Nodes in a Social Network, Data Warehousing and Knowledge Discovery (DaWaK), LNCS 6263 , 52-64, 2010.08.
98. Bin Tong and Einoshin Suzuki, Subclass-oriented Dimension Reduction with Constraint Transformation and Manifold Regularization, Advances in Knowledge Discovery and Data Mining (PAKDD), Part II, LNAI 6119 , 1-13, 2010.06.
99. JianBin Wang, Bin-Hui Chou, Einoshin Suzuki, Finding the k-Most Abnormal Subgraphs from a Single Graph, Discovery Science, Lecture Notes in Artificial Intelligence 5808 (DS) , 441-448, 2009.10.
100. Daisuke Ikeda, Einoshin Suzuki, Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data using Background Texts, Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), Vol. 1, LNAI 5781 , 596-611, 2009.09.
101. Einoshin Suzuki, Shigeru Takano, Hiroshi Hirai, Toward Using Symbolic Discovery in Designing Controllers of Autonomous Swarm Robots, Proc. First International Workshop on LEarning and data Mining for Robotics (LEMIR) , 1-10, 2009.09.
102. Shin Ando, Einoshin Suzuki, Detection of Unique Temporal Segment by Information Theoretic Meta-clustering, Proc. 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 59-68, 2009.06.
103. Einoshin Suzuki, Discovering Action Rules that are Highly Achievable from Massive Data, Advances in Knowledge Discovery and Data Mining (PAKDD), LNAI 5476 , 713-722, 2009.04.
104. Einoshin Suzuki, Negative Encoding Length as a Subjective Interestingness Measure for Groups of Rules, Advances in Knowledge Discovery and Data Mining (PAKDD), LNAI 5476 , 220-231, 2009.04.
105. Shin Ando, Einoshin Suzuki, Unsupervised Cross-domain Learning by Interaction Information Co-clustering, Proc. Eighth IEEE International Conference on Data Mining (ICDM), 13-22, 2008.12.
106. Einoshin Suzuki, Shin Ando, Masayuki Hirose, and Masatoshi Jumi, Intuitive Display for Search Engines toward Fast Detection of Peculiar WWW Pages, Web Intelligence Meets Brain Informatics, State-of-the-Art Survey, Lecture Notes in Artificial Intelligence 4845, pp. 341-352, Springer-Verlag, 2007.10.
107. Masatoshi Jumi, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, Katsuhiko Takabayashi, and Einoshin Suzuki, Spiral Removal of Exceptional Patients for Mining Chronic Hepatitis Data, New Generation Computing, Vol. 25, No. 3, pp. 223-234, 2007.07.
108. Marie Agier, Jean-Marc Petit, and Einoshin Suzuki, Unifying Framework for Rule Semantics: Application to Gene Expression Data, Fundamenta Informaticae, Vol. 78, No. 4, pp. 543-559, 2007.04.
109. Masatoshi Jumi, Einoshin Suzuki, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, and Katsuhiko Takabayashi:, Spiral Discovery of a Separate Prediction Model from Chronic Hepatitis Data, New Frontiers in Artificial Intelligence, Lecture Notes in Artificial Intelligence 3609, Springer-Verlag, pp. 464-473, 2007.04.
110. Shin Ando, Einoshin Suzuki, An Information Theoretic Approach for Detection of Minority Subsets from Database, Proc. Sixth IEEE International Conference on Data Mining (ICDM), pp. 11-20, 2006.12.
111. Yukihiro Nakamura, Shin Ando, Kenji Aoki, Hiroyuki Mano, and Einoshin Suzuki, Strategy Diagram for Identifying Play Strategies in Multi-view Soccer Video Data, Discovery Science, Lecture Notes in Artificial Intelligence, 2006.10.
112. Nicolas Durand, Bruno Cremilleux, and Einoshin Suzuki:, Visualizing Transactional Data with Multiple Clusterings for Knowledge Discovery, Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence (ISMIS), 2006.09.
113. 安藤晋，鈴木英之進, Distributed Multi-objective GA for Generating Comprehensive Pareto Front in Deceptive Optimization Problem, Proc. 2006 IEEE Congress on Evolutionary Computation (IEEE CEC), pp. 5718-5725, 2006.07.
114. 安藤晋，鈴木英之進, Detection of Hostile Web Accesses by One-class Classification Method, Proc. International Workshop on Risk Mining 2006 (RM 2006), pp. 83-93, 2006.06.
115. 広瀬雅之，鈴木英之進, DPITT: Multi-viewpoint Visualization System for Detecting Unexpected WWW Pages Rapidly, 2006 IEEE International Conference on Granular Computing (IEEE-GrC 2006), pp. 538-541, 2006.05.
116. Unsupervised Classification of Relational Data.
117. 青木賢治, 間野裕行, 中村征弘, 安藤晋,鈴木英之進, Mining Multiple Video Clips of a Soccer Game, Proc. First International Workshop on Mining Complex Data (MCD), pp. 17-24, 2005.10.
118. 廣瀬直子，鈴木英之進, Engineering Web Log for Detecting Malicious Sessions to a Web Site by Visual Inspection, WSEAS Transactions on Computers, Issue 10, Vol. 4
WSEAS, 2005.10.
119. 安藤晋, 鈴木英之進, 小林重信, Sample-based Crowding Method for Multimodal Optimization in Continuous Domain, Proc. 2005 IEEE Congress on Evolutionary Computation (IEEE CEC), Vol. 2, pp. 1867-1874, 2005.08.
120. 廣瀬直子，鈴木英之進, Detecting Hostile Accesses to a Web Site Using a Visualization Method Based on Probabilistic Clustering, Proc. Fifth WSEAS International Conference on Simulation, Modeling and Optimization (SMO), pp.596-603, 2005.08.
121. 鈴木英之進，Jan M. Zytkow, Unified Algorithm for Undirected Discovery of Exception Rules, International Journal of Intelligent Systems, Vol.20, No.7, pp.673-691
John Wiley & Sons, 2005.07.
122. Marie Agier, Jean-Marc Petit, 鈴木英之進, Towards Ad-hoc Rule Semantics for Gene Expression Data, Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence 3488 (ISMIS), pp. 494-503, 2005.05.
123. 十見昌俊, 鈴木英之進, 大島宗哲，鍾寧，横井英人，高林克日己, Multi-strategy Instance Selection in Mining Chronic Hepatitis Data, Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence 3488 (ISMIS), 2005.05.
124. 山田悠, 鈴木英之進，横井英人，高林克日己, Experimental Evaluation of Time-series Decision Tree, Active Mining: Second International Workshop, AM 2003, Revised Selected Papers, Lecture Notes in Computer Science 3430, Springer-Verlag, 2005.04.
125. 鈴木英之進, Worst Case and a Distribution-Based Case Analyses of Sampling for Rule Discovery Based on Generality and Accuracy, Journal of Applied intelligence, Vol. 22, No. 1, pp. 29-36
Kluwer Academic Publishers, 2005.01.
126. 広瀬雅之，鈴木英之進, Using WWW-Distribution of Words in Detecting Peculiar Web Pages, Discovery Science, Lecture Notes in Artificial Intelligence 3245 (DS), pp. 355-362, 2004.10.
127. 鈴木英之進, Discovering Interesting Exception Rules with Rule Pair, Proc. ECML/PKDD-2004 Workshop W8 on Advances in Inductive Rule Learning, 2004.09.
128. 鈴木英之進, 鍾寧，横井英人，高林克日己, Spiral Mining of Chronic Hepatitis Data, Proc. ECML/PKDD-2004 Discovery Challenge, pp. 185-196, 2004.09.
129. An Efficient Algorithm for Reducing Clauses Based on Constraint Satisfaction Techniques.
130. 十見昌俊, 鈴木英之進, 大島宗哲，鍾寧，横井英人，高林克日己, Spiral Discovery of a Separate Prediction Model from Chronic Hepatitis Data, Proc. Third International Workshop on Active Mining (AM), pp. 1-10, 2004.06.
131. 鈴木英之進，渡辺健志，横井英人，高林克日己, Detecting Interesting Exceptions from Medical Test Data with Visual Summarization, Proc. Third IEEE International Conference on Data Mining (ICDM-2003), pp. 315-322, 2003.11.
132. 山田悠, 鈴木英之進, 横井英人，高林克日己, Experimental Evaluation of Time-series Decision Tree, Proc. Second International Workshop on Active Mining (AM), pp. 98-105, 2003.10.
133. Jerome Maloberti，鈴木英之進, Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates, Discovery Science, Lecture Notes in Computer Science 2843 (DS-2003), pp. 220-232, 2003.10.
134. 奈良橋正樹，鈴木英之進, Detecting Hostile Accesses through Incremental Subspace Clustering, Proc. 2003 IEEE/WIC International Conference on Web Intelligence (WI-2003), pp. 337-343, 2003.10.
135. 渡辺健志, 鈴木英之進, 横井英人，高林克日己, Application of PrototypeLines to Chronic Hepatitis Data, Proc. ECML/PKDD-2003 Discovery Challenge Workshop Notes, pp. 166-177, 2003.09.
136. 山田悠，鈴木英之進，横井英人，高林克日己, Decision-tree Induction from Time-series Data Based on a Standard-example Split Test, Proc. Twentieth International Conference on Machine Learning (ICML-2003), pp. 840-847, 2003.08.
137. 鈴木英之進, Undirected Discovery of Interesting Exception Rules, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 16, No. 8, pp. 1065-1086
World Scientific, 2002.12.
138. 奈良橋正樹，鈴木英之進, Subspace Clustering Based on Compressibility, Discovery Science, Lecture Notes in Computer Science 2534 (DS-2002), pp. 435-440, 2002.11.
139. 長木悠太，鈴木英之進, Fast Boosting Based on Iterative Data Squashing, Active Mining, New Directions of Data Mining, pp. 151-161
IOS Press, 2002.08.
140. 長木悠太，鈴木英之進, Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance, Principles of Data Mining and Knowledge Discovery, Lecture Notes in Artificial Intelligence 2431 (PKDD-2002), pp. 86-98, 2002.08.
141. 武智文雄，鈴木英之進, Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction, Proc. Nineteenth International Conference on Machine Learning (ICML-2002), pp. 618-625, 2002.07.
142. 稲谷秀太郎，鈴木英之進, Data Squashing for Speeding up Boosting-Based Outlier Detection, Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence 2366 (ISMIS-2002), pp. 601-611, 2002.06.
143. 山田悠，鈴木英之進, Toward Knowledge-Driven Spiral Discovery of Exception Rules, Proc. 2002 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE-2002), Vol. 2, pp. 872-877, 2002.05.
144. 鈴木英之進, In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules, Progresses in Discovery Science, Lecture Notes in Artificial Intelligence 2281, State-of-the-Art Survey, pp. 504-517
Springer-Verlag, 2002.03.
145. David Ramamonjisoa，鈴木英之進，Issam Hamid, Research Topics Discovery from WWW by Keywords Association Rules, Rough Sets and Current Trends in Computing, Lecture Notes in Computer Science 2005 (RSCTC-2000), pp. 412-419, 2001.12.
146. 鈴木英之進, Worst-Case Analysis of Rule Discovery, Discovery Science, Lecture Notes in Artificial Intelligence 2226 (DS-2001), pp. 365-377, 2001.11.
147. 鈴木英之進，後藤匡史，長木悠太, Bloomy Decision Tree for Multi-Objective Classification, Principles of Data Mining and Knowledge Discovery, Lecture Notes in Artificial Intelligence 2168 (PKDD-2001), pp. 436-447, 2001.09.
148. 鈴木英之進, Issues in Organizing a Successful Knowledge Discovery Contest, Discovery Science, Lecture Notes in Artificial Intelligence 1967 (DS-2000), pp. 282-284, 2000.12.
149. 鈴木英之進，Jan M. Zytkow, Unified Algorithm for Undirected Discovery of Exception Rules, Proceedings of PKDD 2000, Lecture Notes in Artificial Intelligence (PKDD-2000), p. 169-180, 2000.09.
150. 鈴木英之進，津本周作, Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets, Knowledge Discovery and Data Mining, Lecture Notes in Artificial Intelligence 1805 (PAKDD-2000), pp. 208-211, 2000.04.
151. Farhad Hussain，Huan Liu，鈴木英之進,Hongjun Lu, Exception Rule Mining with a Relative Interestingness Measure, Knowledge Discovery and Data Mining, Lecture Notes in Artificial Intelligence 1805 (PAKDD-2000), pp. 86-97, 2000.04.
152. 鈴木英之進, Mining Bacterial Test Data with Scheduled Discovery of Exception Rules, Proceedings of International Workshop of KDD Challenge on Real-World Data (KDD Challenge 2000), pp. 34-40, 2000.03.
153. 渡辺浩，鈴木英之進, Circumscribed-Polyhedron Approximation for Maximum Hypersphere Search, Intelligent Agent Technology: Systems, Methodologies, and Tools (IAT-1999), pp. 212-221, 1999.12.
154. 菅谷信介，鈴木英之進, Normal Form Transformation for Object Recognition Based on Support Vector Machines, Discovery Science, Lecture Notes in Artificial Intelligence 1721 (DS-1999), pp. 306-315, 1999.12.
155. 鈴木英之進, Scheduled Discovery of Exception Rules, Discovery Science, Lecture Notes in Artificial Intelligence 1721 (DS-1999), pp. 184-195, 1999.12.
156. 鈴木英之進，石原寛紀, Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density, New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, Lecture Notes in Artificial Intelligence 1711 (RSFDGrC-1999), pp. 414-422, 1999.11.
157. 菅谷信介，鈴木英之進，津本周作, Support Vector Machines for Knowledge Discovery, Principles of Data Mining and Knowledge Discovery, Lecture Notes in Artificial Intelligence 1704 (PKDD-1999), pp. 561-567, 1999.09.
158. 鈴木英之進，大野徹, Prediction Rule Discovery Based on Dynamic Bias Selection, Methodologies for Knowledge Discovery and Data Mining, Lecture Notes in Artificial Intelligence 1574 (PAKDD-1999), pp. 504-508, 1999.04.
159. 津本周作，寺野隆雄，稲田政則，新美礼彦，田崎栄一郎，根岸直矢，酢山明弘，山口高平，橘恵昭，Ju-Zhen Dong，Ning Zhong，大須賀節雄，上石唯貴，菅谷信介，鈴木英之進,塚田誠，猪口明博，, Comparison of Data Mining Methods Using Common Medical Datasets, ISM Symposium: Data Mining and Knowledge Discovery in Data Science, pp. 63-72, 1999.03.
160. 鈴木英之進，Yves Kodratoff, Discovery of Surprising Exception Rules Based on Intensity of Implication, Principles of Data Mining and Knowledge Discovery, Lecture Notes in Artificial Intelligence 1510 (PKDD-1998) Springer-Verlag, pp. 10-18, 1998.09.
161. 鈴木英之進, Simultaneous Reliability Evaluation of Generality and Accuracy for Rule Discovery in Databases, Proceedings of the Fourth International Conference on Knowledge Discovery & Data Mining (KDD-1998), pp. 339-343, 1998.08.
162. 鈴木英之進, Autonomous Discovery of Reliable Exception Rules, Proceedings of the Third International Conference on Knowledge Discovery & Data Mining (KDD-1997), pp. 259-262, 1996.08.
163. 鈴木英之進, Discovering Unexpected Exceptions: A Stochastic Approach, Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery (RSFD-1996), pp. 225-232, 1996.11.
164. 鈴木英之進，志村正道, Exceptional Knowledge Discovery in Databases Based on Information Theory, Proceedings of the Second International Conference on Knowledge Discovery & Data Mining (KDD-1996), pp. 275-27, 1996.08.
165. Pierre Morizet-Mahoudeaux，鈴木英之進，大須賀節雄, Knowledge-Based Handling of Design Expertise, Proceedings of the 10th International Conference on Data Engineering (ICDE-1994), pp. 368-374, 1994.02.
166. 鈴木英之進，阿久津達也，大須賀節雄, Knowledge-Based System for Computer-Aided Drug Design, Knowledge-Based Systems, Vol. 6, No. 2, pp.114-126
Butterworth-Heinemann, 1993.06.
167. Pierre Morizet-Mahoudeaux，鈴木英之進，大須賀節雄，堀浩一，Sundar Kumara，Inyong Ham, Integrated Design and Diagnostics Modeling in Manufacturing: a Prospective Study and First Results, Proceedings of Avignon'92: Twelfth International Conference on Artificial Intelligence, Expert Systems & Natural Language, Vol. 2, pp.235-246, 1992.06.
168. 阿久津達也，鈴木英之進，大須賀節雄, Logic-Based Approach to Expert Systems in Chemistry, Knowledge-Based Systems, Vol. 4, No. 2, pp. 103-116
Butterworth-Heinemann, 1991.06.


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