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ORCIDEinoshin Suzuki
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2020 – today
- 2024
[j31]Ryosuke Miyake, Tetsu Matsukawa, Einoshin Suzuki:
Image Generation from Hyper Scene Graph with Multiple Types of Trinomial Hyperedges. SN Comput. Sci. 5(5): 624 (2024)- [c121]Tetsu Matsukawa
, Einoshin Suzuki:
A Robust Person Shape Representation via Grassmann Channel Pooling. ICPR (8) 2024: 455-474 - [c120]Liheng Shen, Tetsu Matsukawa, Einoshin Suzuki:
SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection. PAKDD (1) 2024: 27-40 - [c119]Ryosuke Miyake, Tetsu Matsukawa, Einoshin Suzuki:
Image Generation from Hyper Scene Graphs with Trinomial Hyperedges Using Object Attention. VISIGRAPP (2): VISAPP 2024: 266-279 - 2023
- [j30]Kang Zhang, Muhammad Fikko Fadjrimiratno, Einoshin Suzuki:
Region Anomaly Detection via Spatial and Semantic Attributed Graph in Human Monitoring. Sensors 23(3): 1307 (2023) - [c118]Jose Alejandro Avellaneda, Tetsu Matsukawa, Einoshin Suzuki:
Cross-Modal Self-Supervised Feature Extraction for Anomaly Detection in Human Monitoring. CASE 2023: 1-8 - [c117]Wenbo Li, Yao Yang, Einoshin Suzuki:
Class-Specific Word Sense Aware Topic Modeling via Soft Orthogonalized Topics. CIKM 2023: 1218-1227 - [c116]Qiming Zou, Einoshin Suzuki:
Sample-Efficient Goal-Conditioned Reinforcement Learning via Predictive Information Bottleneck for Goal Representation Learning. ICRA 2023: 9523-9529 - [c115]Kang Zhang, Einoshin Suzuki:
Judging Credible and Unethical Statistical Data Explanations via Phrase Similarity Graph. PACIS 2023: 121 - [c114]Ryosuke Miyake, Tetsu Matsukawa, Einoshin Suzuki:
Image Generation from a Hyper Scene Graph with Trinomial Hyperedges. VISIGRAPP (5: VISAPP) 2023: 185-195 - 2022
- [j29]Ning Dong, Einoshin Suzuki:
GIAD-ST: Detecting anomalies in human monitoring based on generative inpainting via self-supervised multi-task learning. J. Intell. Inf. Syst. 59(3): 733-754 (2022) - [c113]Kang Zhang, Hiroaki Shinden, Tatsuki Mutsuro, Einoshin Suzuki:
Judging Instinct Exploitation in Statistical Data Explanations Based on Word Embedding. AIES 2022: 867-879 - [c112]Liheng Shen, Tetsu Matsukawa, Einoshin Suzuki:
Detecting Video Anomalous Events with an Enhanced Abnormality Score. PRICAI (1) 2022: 202-217 - 2021
- [j28]Mirai Takayanagi, Yasuo Tabei, Einoshin Suzuki, Hiroto Saigo:
Sparse Nonnegative Interaction Models. IEEE Access 9: 109994-110005 (2021) - [j27]Wenbo Li, Einoshin Suzuki:
Adaptive and hybrid context-aware fine-grained word sense disambiguation in topic modeling based document representation. Inf. Process. Manag. 58(4): 102592 (2021) - [j26]Wenbo Li, Hiroto Saigo, Bin Tong, Einoshin Suzuki:
Topic modeling for sequential documents based on hybrid inter-document topic dependency. J. Intell. Inf. Syst. 56(3): 435-458 (2021) - [c111]Qiming Zou, Einoshin Suzuki:
Contrastive Goal Grouping for Policy Generalization in Goal-Conditioned Reinforcement Learning. ICONIP (1) 2021: 240-253 - [c110]Kang Zhang, Muhammad Fikko Fadjrimiratno, Einoshin Suzuki:
Context-Based Anomaly Detection via Spatial Attributed Graphs in Human Monitoring. ICONIP (1) 2021: 450-463 - [c109]Ning Dong, Einoshin Suzuki:
GIAD: Generative Inpainting-Based Anomaly Detection via Self-Supervised Learning for Human Monitoring. PRICAI (2) 2021: 418-432 - [c108]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. VISIGRAPP (5: VISAPP) 2021: 943-953 - [i2]Yusuke Ohtsubo, Tetsu Matsukawa, Einoshin Suzuki:
Semi-Supervised Few-Shot Classification with Deep Invertible Hybrid Models. CoRR abs/2105.10644 (2021) - 2020
- [j25]Hirofumi Fujita, Tetsu Matsukawa, Einoshin Suzuki:
Detecting outliers with one-class selective transfer machine. Knowl. Inf. Syst. 62(5): 1781-1818 (2020) - [j24]Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, Yoichi Sato:
Hierarchical Gaussian Descriptors with Application to Person Re-Identification. IEEE Trans. Pattern Anal. Mach. Intell. 42(9): 2179-2194 (2020) - [c107]Wenbo Li, Einoshin Suzuki:
Hybrid Context-Aware Word Sense Disambiguation in Topic Modeling based Document Representation. ICDM 2020: 332-341 - [c106]Kaikai Zhao, Takashi Imaseki, Hiroshi Mouri, Einoshin Suzuki, Tetsu Matsukawa:
From Certain to Uncertain: Toward Optimal Solution for Offline Multiple Object Tracking. ICPR 2020: 2506-2513 - [c105]Tetsu Matsukawa, Einoshin Suzuki:
Convolutional Feature Transfer via Camera-Specific Discriminative Pooling for Person Re-Identification. ICPR 2020: 8408-8415 - [c104]Ning Dong, Yusuke Hatae, Muhammad Fikko Fadjrimiratno, Tetsu Matsukawa, Einoshin Suzuki:
Experimental Evaluation of GAN-Based One-Class Anomaly Detection on Office Monitoring. ISMIS 2020: 214-224 - [c103]Wenbo Li, Tetsu Matsukawa, Hiroto Saigo, Einoshin Suzuki:
Context-Aware Latent Dirichlet Allocation for Topic Segmentation. PAKDD (1) 2020: 475-486 - [c102]Yusuke Hatae, Qingpu Yang, Muhammad Fikko Fadjrimiratno, Yuanyuan Li, Tetsu Matsukawa, Einoshin Suzuki:
Detecting Anomalous Regions from an Image based on Deep Captioning. VISIGRAPP (5: VISAPP) 2020: 326-335
2010 – 2019
- 2019
- [j23]Kaikai Zhao, Tetsu Matsukawa, Einoshin Suzuki:
Experimental validation for N-ary error correcting output codes for ensemble learning of deep neural networks. J. Intell. Inf. Syst. 52(2): 367-392 (2019) - [c101]Yusuke Ohtsubo, Tetsu Matsukawa, Einoshin Suzuki:
Harnessing GAN with Metric Learning for One-Shot Generation on a Fine-Grained Category. ICTAI 2019: 915-922 - [c100]Tetsu Matsukawa, Einoshin Suzuki:
Kernelized Cross-view Quadratic Discriminant Analysis for Person Re-Identification. MVA 2019: 1-5 - 2018
- [c99]Einoshin Suzuki:
Exploiting Micro-Clusters to Close The Loop in Data-Mining Robots for Human Monitoring. AAAI Spring Symposia 2018 - [c98]Kaikai Zhao, Tetsu Matsukawa, Einoshin Suzuki:
Retraining: A Simple Way to Improve the Ensemble Accuracy of Deep Neural Networks for Image Classification. ICPR 2018: 860-867 - [c97]Soichiro Oura, Tetsu Matsukawa, Einoshin Suzuki:
Multimodal Deep Neural Network with Image Sequence Features for Video Captioning. IJCNN 2018: 1-7 - [c96]Hirofumi Fujita, Tetsu Matsukawa, Einoshin Suzuki:
One-class Selective Transfer Machine for Personalized Anomalous Facial Expression Detection. VISIGRAPP (5: VISAPP) 2018: 274-283 - 2017
- [j22]Yutaka Deguchi, Daisuke Takayama, Shigeru Takano, Vasile-Marian Scuturici, Jean-Marc Petit, Einoshin Suzuki:
Skeleton clustering by multi-robot monitoring for fall risk discovery. J. Intell. Inf. Syst. 48(1): 75-115 (2017) - [i1]Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, Yoichi Sato:
Hierarchical Gaussian Descriptors with Application to Person Re-Identification. CoRR abs/1706.04318 (2017) - 2016
- [j21]Shin Ando, Einoshin Suzuki:
Minimizing response time in time series classification. Knowl. Inf. Syst. 46(2): 449-476 (2016) - [c95]Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, Yoichi Sato:
Hierarchical Gaussian Descriptor for Person Re-identification. CVPR 2016: 1363-1372 - [c94]Tetsu Matsukawa, Einoshin Suzuki:
Person re-identification using CNN features learned from combination of attributes. ICPR 2016: 2428-2433 - 2015
- [j20]Shin Ando, Theerasak Thanomphongphan, Yoichi Seki, Einoshin Suzuki:
Ensemble anomaly detection from multi-resolution trajectory features. Data Min. Knowl. Discov. 29(1): 39-83 (2015) - [j19]Somar Boubou, Einoshin Suzuki:
Classifying actions based on histogram of oriented velocity vectors. J. Intell. Inf. Syst. 44(1): 49-65 (2015) - [c93]Vasile-Marian Scuturici, Yann Gripay, Jean-Marc Petit, Yutaka Deguchi, Einoshin Suzuki:
Continuous Query Processing Over Data, Streams and Services: Application to Robotics. ADBIS (Short Papers and Workshops) 2015: 36-43 - [c92]Yutaka Deguchi, Einoshin Suzuki:
Hidden Fatigue Detection for a Desk Worker Using Clustering of Successive Tasks. AmI 2015: 268-283 - [c91]Somar Boubou, A. H. Abdul Hafez, Einoshin Suzuki:
Visual impression localization of autonomous robots. CASE 2015: 328-334 - [c90]Einoshin Suzuki:
On the Feasibility of Discovering Meta-Patterns from a Data Ensemble. Discovery Science 2015: 266-274 - [c89]Kaikai Zhao, Einoshin Suzuki:
Clustering Classifiers Learnt from Local Datasets Based on Cosine Similarity. ISMIS 2015: 150-159 - [c88]Einoshin Suzuki, Yutaka Deguchi, Tetsu Matsukawa, Shin Ando, Hiroaki Ogata, Masanori Sugimoto:
Toward a platform for collecting, mining, and utilizing behavior data for detecting students with depression risks. PETRA 2015: 26:1-26:8 - 2014
- [j18]Bin Tong, Junbin Gao, Thach Huy Nguyen, Hao Shao, Einoshin Suzuki:
Transfer dimensionality reduction by Gaussian process in parallel. Knowl. Inf. Syst. 38(3): 567-597 (2014) - [j17]Daisuke Ikeda, Einoshin Suzuki:
Finding peculiar compositions of two frequent strings with background texts. Knowl. Inf. Syst. 41(2): 499-530 (2014) - [c87]Ryosuke Kondo, Yutaka Deguchi, Einoshin Suzuki:
Developing a Face Monitoring Robot for a Desk Worker. AmI 2014: 226-241 - [c86]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. AmI 2014: 258-273 - [c85]Angdy Erna, Linli Yu, Kaikai Zhao, Wei Chen, Einoshin Suzuki:
Facial Expression Data Constructed with Kinect and Their Clustering Stability. AMT 2014: 421-431 - [c84]Bin Tong, Tetsuro Morimura, Einoshin Suzuki, Tsuyoshi Idé:
Probabilistic Two-Level Anomaly Detection for Correlated Systems. ECAI 2014: 1109-1110 - [c83]Shin Ando, Einoshin Suzuki:
Discriminative Learning on Exemplary Patterns of Sequential Numerical Data. ICDM 2014: 1-10 - [c82]Yutaka Deguchi, Einoshin Suzuki:
Skeleton Clustering by Autonomous Mobile Robots for Subtle Fall Risk Discovery. ISMIS 2014: 500-505 - [c81]Yutaka Deguchi, Daisuke Takayama, Shigeru Takano, Vasile-Marian Scuturici, Jean-Marc Petit, Einoshin Suzuki:
Multiple-robot monitoring system based on a service-oriented DBMS. PETRA 2014: 47:1-47:8 - 2013
- [j16]Einoshin Suzuki:
Special Issue on Discovery Science: Guest Editor's Introduction. Comput. J. 56(3): 271-273 (2013) - [j15]Thach Huy Nguyen, Bin Tong, Hao Shao, Einoshin Suzuki:
Transfer learning by centroid pivoted mapping in noisy environment. J. Intell. Inf. Syst. 41(1): 39-60 (2013) - [j14]Hao Shao, Bin Tong, Einoshin Suzuki:
Extended MDL principle for feature-based inductive transfer learning. Knowl. Inf. Syst. 35(2): 365-389 (2013) - [j13]Thach Huy Nguyen, Hao Shao, Bin Tong, Einoshin Suzuki:
A feature-free and parameter-light multi-task clustering framework. Knowl. Inf. Syst. 36(1): 251-276 (2013) - [j12]Bin-Hui Chou, Einoshin Suzuki:
RoClust: Role discovery for graph clustering. Web Intell. Agent Syst. 11(1): 1-20 (2013) - [c80]Shigeru Takano, Ilya Loshchilov, David Meunier, Michèle Sebag, Einoshin Suzuki:
Fast Adaptive Object Detection towards a Smart Environment by a Mobile Robot. AmI 2013: 182-197 - [c79]Bin-Hui Chou, Einoshin Suzuki:
Detecting Academic Plagiarism with Graphs. EGC 2013: 293-304 - [c78]Einoshin Suzuki, Yutaka Deguchi, Daisuke Takayama, Shigeru Takano, Vasile-Marian Scuturici, Jean-Marc Petit:
Towards Facilitating the Development of Monitoring Systems with Low-Cost Autonomous Mobile Robots. ISIP 2013: 57-70 - [c77]Shin Ando, Einoshin Suzuki:
Time-sensitive Classification of Behavioral Data. SDM 2013: 458-466 - 2012
- [j11]Bin Tong, Weifeng Jia, Yanli Ji, Einoshin Suzuki:
Linear Semi-Supervised Dimensionality Reduction with Pairwise Constraint for Multiple Subclasses. IEICE Trans. Inf. Syst. 95-D(3): 812-820 (2012) - [j10]Bin Tong, Hao Shao, Bin-Hui Chou, Einoshin Suzuki:
Linear semi-supervised projection clustering by transferred centroid regularization. J. Intell. Inf. Syst. 39(2): 461-490 (2012) - [c76]Hao Shao, Bin Tong, Einoshin Suzuki:
Query by Committee in a Heterogeneous Environment. ADMA 2012: 186-198 - [c75]Einoshin Suzuki, Emi Matsumoto, Asuki Kouno:
Data Squashing for HSV Subimages by an Autonomous Mobile Robot. Discovery Science 2012: 95-109 - [c74]Kouhei Takemoto, Shigeru Takano, Einoshin Suzuki:
Human Detection by a Small Autonomous Mobile Robot. EGC 2012: 531-536 - [c73]Asuki Kouno, Daisuke Takayama, Einoshin Suzuki:
Predicting the State of a Person by an Office-Use Autonomous Mobile Robot. IAT 2012: 80-84 - [c72]Shinsuke Sugaya, Daisuke Takayama, Asuki Kouno, Einoshin Suzuki:
Intelligent Data Analysis by a Home-Use Human Monitoring Robot. IDA 2012: 381-391 - 2011
- [c71]Bin-Hui Chou, Einoshin Suzuki:
Role Discovery for Graph Clustering. APWeb 2011: 17-28 - [c70]Hiroshi Hirai, Bin-Hui Chou, Einoshin Suzuki:
A Parameter-Free Method for Discovering Generalized Clusters in a Network. Discovery Science 2011: 135-149 - [c69]Asuki Kouno, Jean-Marc Montanier, Shigeru Takano, Nicolas Bredèche, Marc Schoenauer, Michèle Sebag, Einoshin Suzuki:
On-Board Evolutionary Algorithm and Off-Line Rule Discovery for Column Formation in Swarm Robotics. IAT 2011: 220-227 - [c68]Shin Ando, Einoshin Suzuki:
Role-Behavior Analysis from Trajectory Data by Cross-Domain Learning. ICDM 2011: 21-30 - [c67]Somar Boubou, Asuki Kouno, Einoshin Suzuki:
Implementing Camshift on a Mobile Robot for Person Tracking and Pursuit. ICDM Workshops 2011: 682-688 - [c66]Shigeru Takano, Einoshin Suzuki:
New Object Detection for On-board Robot Vision by Lifting Complex Wavelet Transforms. ICDM Workshops 2011: 911-916 - [c65]Emi Matsumoto, Michèle Sebag, Einoshin Suzuki:
Using SVM to Avoid Humans: A Case of a Small Autonomous Mobile Robot in an Office. ISCIS 2011: 283-287 - [c64]Thach Huy Nguyen, Hao Shao, Bin Tong, Einoshin Suzuki:
A Compression-Based Dissimilarity Measure for Multi-task Clustering. ISMIS 2011: 123-132 - [c63]Hao Shao, Bin Tong, Einoshin Suzuki:
Compact Coding for Hyperplane Classifiers in Heterogeneous Environment. ECML/PKDD (3) 2011: 207-222 - [c62]Shin Ando, Einoshin Suzuki, Yoichi Seki, Theerasak Thanongphongphan, Daisuke Hoshino:
ACE: Anomaly Clustering Ensemble for Multi-perspective Anomaly Detection in Robot Behaviors. SDM 2011: 1-12 - [c61]Hao Shao, Einoshin Suzuki:
Feature-based Inductive Transfer Learning through Minimum Encoding. SDM 2011: 259-270 - [c60]Bin Tong, Junbin Gao, Thach Huy Nguyen, Einoshin Suzuki:
Gaussian Process for Dimensionality Reduction in Transfer Learning. SDM 2011: 783-794 - [e6]Jomi Fred Hübner, Jean-Marc Petit, Einoshin Suzuki:
Proceedings of the 2011 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2011, Campus Scientifique de la Doua, Lyon, France, August 22-27, 2011. IEEE Computer Society 2011 [contents] - 2010
- [j9]Takashi Washio, Einoshin Suzuki, Kai Ming Ting:
Best papers from the 12th Pacific-Asia conference on knowledge discovery and data mining (PAKDD2008). Knowl. Inf. Syst. 25(2): 209-210 (2010) - [c59]Asuki Kouno, Shigeru Takano, Einoshin Suzuki:
Constructing Low-Cost Swarm Robots That March in Column Formation. ANTS Conference 2010: 556-557 - [c58]Bin-Hui Chou, Einoshin Suzuki:
Discovering Community-Oriented Roles of Nodes in a Social Network. DaWak 2010: 52-64 - [c57]Bin Tong, Zhiguang Qin, Einoshin Suzuki:
Topology Preserving SOM with Transductive Confidence Machine. Discovery Science 2010: 27-41 - [c56]Swagat Kumar, Thach Huy Nguyen, Einoshin Suzuki:
Understanding the Behaviour of Reactive Robots in a Patrol Task by Analysing Their Trajectories. IAT 2010: 56-63 - [c55]Bin Tong, Einoshin Suzuki:
Subclass-Oriented Dimension Reduction with Constraint Transformation and Manifold Regularization. PAKDD (2) 2010: 1-13 - [c54]Bin Tong, Hao Shao, Bin-Hui Chou, Einoshin Suzuki:
Semi-supervised Projection Clustering with Transferred Centroid Regularization. ECML/PKDD (3) 2010: 306-321
2000 – 2009
- 2009
- [c53]JianBin Wang, Bin-Hui Chou, Einoshin Suzuki:
Finding the k-Most Abnormal Subgraphs from a Single Graph. Discovery Science 2009: 441-448 - [c52]Hiroshi Hirai, Shigeru Takano, Einoshin Suzuki:
Simulating Swarm Robots for Collision Avoidance Problem Based on a Dynamic Bayesian Network. ECAL (2) 2009: 416-423 - [c51]Einoshin Suzuki:
Compression-Based Measures for Mining Interesting Rules. IEA/AIE 2009: 741-746 - [c50]Shin Ando, Einoshin Suzuki:
Detection of unique temporal segments by information theoretic meta-clustering. KDD 2009: 59-68 - [c49]Einoshin Suzuki:
Negative Encoding Length as a Subjective Interestingness Measure for Groups of Rules. PAKDD 2009: 220-231 - [c48]Einoshin Suzuki:
Discovering Action Rules That Are Highly Achievable from Massive Data. PAKDD 2009: 713-722 - [c47]Daisuke Ikeda, Einoshin Suzuki:
Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data Using Background Texts. ECML/PKDD (1) 2009: 596-611 - 2008
- [c46]Shin Ando, Einoshin Suzuki:
Unsupervised Cross-Domain Learning by Interaction Information Co-clustering. ICDM 2008: 13-22 - [p1]Einoshin Suzuki:
Pitfalls for Categorizations of Objective Interestingness Measures for Rule Discovery. Statistical Implicative Analysis 2008: 383-395 - [e5]Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi:
Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008 Proceedings. Lecture Notes in Computer Science 5012, Springer 2008, ISBN 978-3-540-68124-3 [contents] - [e4]Régis Gras, Einoshin Suzuki, Fabrice Guillet, Filippo Spagnolo:
Statistical Implicative Analysis, Theory and Applications. Studies in Computational Intelligence 127, Springer 2008, ISBN 978-3-540-78982-6 [contents] - 2007
- [j8]Marie Agier, Jean-Marc Petit, Einoshin Suzuki:
Unifying Framework for Rule Semantics: Application to Gene Expression Data. Fundam. Informaticae 78(4): 543-559 (2007) - [j7]Masatoshi Jumi, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, Katsuhiko Takabayashi, Einoshin Suzuki:
Spiral Removal of Exceptional Patients for Mining Chronic Hepatitis Data. New Gener. Comput. 25(3): 223-234 (2007) - [c45]Einoshin Suzuki:
Peut-on Capturer la Sémantique à Travers la Syntaxe ? - Découverte des Règles d'Exception Simultanée. EGC 2007: 1 - [c44]Régis Gras, Pascale Kuntz, Einoshin Suzuki:
Une règle d'exception en Analyse Statistique Implicative. EGC 2007: 87-98 - [e3]Vincent Corruble, Masayuki Takeda, Einoshin Suzuki:
Discovery Science, 10th International Conference, DS 2007, Sendai, Japan, October 1-4, 2007, Proceedings. Lecture Notes in Computer Science 4755, Springer 2007, ISBN 978-3-540-75487-9 [contents] - 2006
- [j6]Einoshin Suzuki:
Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table. J. Univers. Comput. Sci. 12(6): 627-653 (2006) - [c43]Shin Ando, Einoshin Suzuki:
Distributed Multi-objective GA for Generating Comprehensive Pareto Front in Deceptive Optimization Problems. IEEE Congress on Evolutionary Computation 2006: 1569-1576 - [c42]Yukihiro Nakamura, Shin Ando, Kenji Aoki, Hiroyuki Mano, Einoshin Suzuki:
Strategy Diagram for Identifying Play Strategies in Multi-view Soccer Video Data. Discovery Science 2006: 173-184 - [c41]Jérôme Maloberti, Shin Ando, Einoshin Suzuki:
Classification non-supervisée de données relationnelles. EGC 2006: 389-390 - [c40]Masayuki Hirose, Einoshin Suzuki:
DPITT: Multi-viewpoint Visualization System for Detecting Peculiar WWW Pages Rapidly. GrC 2006: 538-541 - [c39]Shin Ando, Einoshin Suzuki:
An Information Theoretic Approach to Detection of Minority Subsets in Database. ICDM 2006: 11-20 - [c38]Nicolas Durand, Bruno Crémilleux, Einoshin Suzuki:
Visualizing Transactional Data with Multiple Clusterings for Knowledge Discovery. ISMIS 2006: 47-57 - [c37]Einoshin Suzuki, Shin Ando, Masayuki Hirose, Masatoshi Jumi:
Intuitive Display for Search Engines Toward Fast Detection of Peculiar WWW Pages. WImBI 2006: 341-352 - 2005
- [j5]Einoshin Suzuki:
Worst Case and a Distribution-Based Case Analyses of Sampling for Rule Discovery Based on Generality and Accuracy. Appl. Intell. 22(1): 29-36 (2005) - [j4]Einoshin Suzuki, Jan M. Zytkow:
Unified algorithm for undirected discovery of exception rules. Int. J. Intell. Syst. 20(7): 673-691 (2005) - [c36]Shin Ando, Einoshin Suzuki, Shigenobu Kobayashi:
Sample based crowding method for multimodal optimization in continuous domain. Congress on Evolutionary Computation 2005: 1867-1874 - [c35]Masanori Yoshinaga, Yukihiro Nakamura, Einoshin Suzuki:
Mini-Car-Soccer as a testbed for granular computing. GrC 2005: 92-97 - [c34]Masatoshi Jumi, Einoshin Suzuki, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, Katsuhiko Takabayashi:
Multi-strategy Instance Selection in Mining Chronic Hepatitis Data. ISMIS 2005: 475-484 - [c33]Marie Agier, Jean-Marc Petit, Einoshin Suzuki:
Towards Ad-Hoc Rule Semantics for Gene Expression Data. ISMIS 2005: 494-503 - 2004
- [c32]Einoshin Suzuki:
Undirected Exception Rule Discovery as Local Pattern Detection. Local Pattern Detection 2004: 207-216 - [c31]Masayuki Hirose, Einoshin Suzuki:
Using WWW-Distribution of Words in Detecting Peculiar Web Pages. Discovery Science 2004: 355-362 - [c30]Jérôme Maloberti, Einoshin Suzuki:
An Efficient Algorithm for Reducing Clauses Based on Constraint Satisfaction Techniques. ILP 2004: 234-251 - [c29]Masatoshi Jumi, Einoshin Suzuki, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, Katsuhiko Takabayashi:
Spiral Discovery of a Separate Prediction Model from Chronic Hepatitis Data. JSAI Workshops 2004: 464-473 - [e2]Einoshin Suzuki, Setsuo Arikawa:
Discovery Science, 7th International Conference, DS 2004, Padova, Italy, October 2-5, 2004, Proceedings. Lecture Notes in Computer Science 3245, Springer 2004, ISBN 3-540-23357-1 [contents] - 2003
- [c28]Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi:
Experimental Evaluation of Time-Series Decision Tree. Active Mining 2003: 190-209 - [c27]Jérôme Maloberti, Einoshin Suzuki:
Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates. Discovery Science 2003: 220-232 - [c26]Einoshin Suzuki, Takeshi Watanabe, Hideto Yokoi, Katsuhiko Takabayashi:
Detecting Interesting Exceptions from Medical Test Data with Visual Summarization. ICDM 2003: 315-322 - [c25]Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi:
Decision-tree Induction from Time-series Data Based on a Standard-example Split Test. ICML 2003: 840-847 - [c24]Masaki Narahashi, Einoshin Suzuki:
Detecting Hostile Accesses through Incremental Subspace Clustering. Web Intelligence 2003: 337-343 - [e1]Ning Zhong, Zbigniew W. Ras, Shusaku Tsumoto, Einoshin Suzuki:
Foundations of Intelligent Systems, 14th International Symposium, ISMIS 2003, Maebashi City, Japan, October 28-31, 2003, Proceedings. Lecture Notes in Computer Science 2871, Springer 2003, ISBN 3-540-20256-0 [contents] - 2002
- [j3]Einoshin Suzuki:
Undirected Discovery of Interesting Exception Rules. Int. J. Pattern Recognit. Artif. Intell. 16(8): 1065-1086 (2002) - [c23]Masaki Narahashi, Einoshin Suzuki:
Subspace Clustering Based on Compressibility. Discovery Science 2002: 435-440 - [c22]Einoshin Suzuki:
In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules. Progress in Discovery Science 2002: 504-517 - [c21]Yuu Yamada, Einoshin Suzuki:
Toward knowledge-driven spiral discovery of exception rules. FUZZ-IEEE 2002: 872-877 - [c20]Fumio Takechi, Einoshin Suzuki:
Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction. ICML 2002: 618-625 - [c19]Shutaro Inatani, Einoshin Suzuki:
Data Squashing for Speeding Up Boosting-Based Outlier Detection. ISMIS 2002: 601-612 - [c18]Yuta Choki, Einoshin Suzuki:
Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance. PKDD 2002: 86-98 - 2001
- [c17]Einoshin Suzuki:
Worst-Case Analysis of Rule Discovery. Discovery Science 2001: 365-377 - [c16]Einoshin Suzuki, Masafumi Gotoh, Yuta Choki:
Bloomy Decision Tree for Multi-objective Classification. PKDD 2001: 436-447 - 2000
- [c15]Einoshin Suzuki:
Issues in Organizing a Successful Knowledge Discovery Contest. Discovery Science 2000: 282-284 - [c14]Farhad Hussain, Huan Liu, Einoshin Suzuki, Hongjun Lu:
Exception Rule Mining with a Relative Interestingness Measure. PAKDD 2000: 86-97 - [c13]Einoshin Suzuki, Shusaku Tsumoto:
Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets. PAKDD 2000: 208-211 - [c12]Einoshin Suzuki, Jan M. Zytkow:
Unified Algorithm for Undirected Discovery of Execption Rules. PKDD 2000: 169-180 - [c11]David Ramamonjisoa, Einoshin Suzuki, Issam A. Hamid:
Research Topics Discovery from WWW by Keywords Association Rules. Rough Sets and Current Trends in Computing 2000: 412-419
1990 – 1999
- 1999
- [c10]Einoshin Suzuki:
Scheduled Discovery of Exception Rules. Discovery Science 1999: 184-195 - [c9]Shinsuke Sugaya, Einoshin Suzuki:
Normal Form Transformation for Object Recognition Based on Support Vector Machines. Discovery Science 1999: 306-315 - [c8]Einoshin Suzuki, Toru Ohno:
Prediction Rule Discovery Based on Dynamic Bias Selection. PAKDD 1999: 504-508 - [c7]Shinsuke Sugaya, Einoshin Suzuki, Shusaku Tsumoto:
Support Vector Machines for Knowledge Discovery. PKDD 1999: 561-567 - [c6]Einoshin Suzuki, Hiroki Ishihara:
Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density. RSFDGrC 1999: 414-422 - 1998
- [c5]Einoshin Suzuki:
Simultaneous Reliability Evaluation of Generality and Accuracy for Rule Discovery in Databases. KDD 1998: 339-343 - [c4]Einoshin Suzuki, Yves Kodratoff:
Discovery of Surprising Exception Rules Based on Intensity of Implication. PKDD 1998: 10-18 - 1997
- [c3]Einoshin Suzuki:
Autonomous Discovery of Reliable Exception Rules. KDD 1997: 259-262 - 1996
- [c2]Einoshin Suzuki, Masamichi Shimura:
Exceptional Knowledge Discovery in Databases Based on Information Theory. KDD 1996: 275-278 - 1994
- [c1]Pierre Morizet-Mahoudeaux, Einoshin Suzuki, Setsuo Ohsuga:
Knowledge-Based Handling of Design Expertise. ICDE 1994: 368-374 - 1993
- [j2]Einoshin Suzuki, Tatsuya Akutsu, Setsuo Ohsuga:
Knowledge-based system for computer-aided drug design. Knowl. Based Syst. 6(2): 114-126 (1993) - 1991
- [j1]Tatsuya Akutsu, Einoshin Suzuki, Setsuo Ohsuga:
Logic-based approach to expert systems in chemistry. Knowl. Based Syst. 4(2): 103-116 (1991)
Coauthor Index
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