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Note: Links lead to the DBLP on the Web. Hiroshi Motoda Phu Chien Nguyen , Kouzou Ohara , Hiroshi Motoda, Takashi Washio : Cl-GBI: A Novel Approach for Extracting Typical Patterns from Graph-Structured Data. PAKDD 2005 : 639-649 Kenichi Yoshida , Fuminori Adachi , Takashi Washio , Hiroshi Motoda, Teruaki Homma , Akihiro Nakashima , Hiromitsu Fujikawa , Katsuyuki Yamazaki : Memory Management of Density-Based Spam Detector. SAINT 2005 : 370-376 Takashi Washio , Atsushi Fujimoto , Hiroshi Motoda: A Framework of Numerical Basket Analysis. SAINT Workshops 2005 : 340-343 Kouzou Ohara , Yukio Onishi , Noboru Babaguchi , Hiroshi Motoda: Constructive Inductive Learning Based on Meta-attributes. Discovery Science 2004 : 142-154 Kenichi Yoshida , Fuminori Adachi , Takashi Washio , Hiroshi Motoda, Teruaki Homma , Akihiro Nakashima , Hiromitsu Fujikawa , Katsuyuki Yamazaki : Density-based spam detector. KDD 2004 : 486-493 Katsutoshi Yada , Hiroshi Motoda, Takashi Washio , Asuka Miyawaki : Consumer Behavior Analysis by Graph Mining Technique. KES 2004 : 800-806 Amit Mandvikar , Huan Liu , Hiroshi Motoda: Compact Dual Ensembles for Active Learning. PAKDD 2004 : 293-297 Phu Chien Nguyen , Takashi Washio , Kouzou Ohara , Hiroshi Motoda: Using a Hash-Based Method for Apriori-Based Graph Mining. PKDD 2004 : 349-361 Huan Liu , Hiroshi Motoda, Lei Yu : A selective sampling approach to active feature selection. Artif. Intell. 159 (1-2): 49-74 (2004) Nada Lavrac , Hiroshi Motoda, Tom Fawcett , Robert Holte , Pat Langley , Pieter W. Adriaans : Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. Machine Learning 57 (1-2): 13-34 (2004) Nada Lavrac , Hiroshi Motoda, Tom Fawcett : Editorial: Data Mining Lessons Learned. Machine Learning 57 (1-2): 5-11 (2004) Warodom Geamsakul , Takashi Matsuda , Tetsuya Yoshida , Hiroshi Motoda, Takashi Washio : Performance Evaluation of Decision Tree Graph-Based Induction. Discovery Science 2003 : 128-140 Fuminori Adachi , Takashi Washio , Hiroshi Motoda, Atsushi Fujimoto , Hidemitsu Hanafusa : Development of Generic Search Method Based on Transformation Invariance. ISMIS 2003 : 486-495 Huan Liu , Lei Yu , Manoranjan Dash , Hiroshi Motoda: Active Feature Selection Using Classes. PAKDD 2003 : 474-485 Warodom Geamsakul , Takashi Matsuda , Tetsuya Yoshida , Hiroshi Motoda, Takashi Washio : Classifier Construction by Graph-Based Induction for Graph-Structured Data. PAKDD 2003 : 52-62 Akihiro Inokuchi , Takashi Washio , Hiroshi Motoda: Complete Mining of Frequent Patterns from Graphs: Mining Graph Data. Machine Learning 50 (3): 321-354 (2003) Takashi Washio , Hiroshi Motoda: State of the art of graph-based data mining. SIGKDD Explorations 5 (1): 59-68 (2003) Setsuo Arikawa , Koichi Furukawa , Shinichi Morishita , Hiroshi Motoda: Preface. Theor. Comput. Sci. 292 (2): 343-344 (2003) Takashi Matsuda , Hiroshi Motoda, Tetsuya Yoshida , Takashi Washio : Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction. Discovery Science 2002 : 422-429 Tetsuya Yoshida , Hiroshi Motoda, Takashi Washio : Adaptive Ripple Down Rules Method based on Minimum Description Length Principle. ICDM 2002 : 530-537 Huan Liu , Hiroshi Motoda, Lei Yu : Feature Selection with Selective Sampling. ICML 2002 : 395-402 Takuya Wada , Tetsuya Yoshida , Hiroshi Motoda, Takashi Washio : Extension of the RDR Method That Can Adapt to Environmental Changes and Acquire Knowledge from Both Experts and Data. PRICAI 2002 : 218-227 Keisei Fujiwara , Tetsuya Yoshida , Hiroshi Motoda, Takashi Washio : Case Generation Method for Constructing an RDR Knowledge Base. PRICAI 2002 : 228-237 Takashi Matsuda , Hiroshi Motoda, Tetsuya Yoshida , Takashi Washio : Knowledge Discovery from Structured Data by Beam-Wise Graph-Based Induction. PRICAI 2002 : 255-264 Takashi Washio , Hiroshi Motoda: Toward the Discovery of First Principle Based Scientific Law Equations. Progress in Discovery Science 2002 : 553-564 Huan Liu , Hiroshi Motoda: On Issues of Instance Selection. Data Min. Knowl. Discov. 6 (2): 115-130 (2002) Masahiro Terabe , Takashi Washio , Hiroshi Motoda, Osamu Katai , Tetsuo Sawaragi : Attribute Generation Based on Association Rules. Knowl. Inf. Syst. 4 (3): 329-349 (2002) Takashi Washio , Hiroshi Motoda, Yuji Niwa : Discovering Admissible Simultaneous Equation Models from Observed Data. ECML 2001 : 539-551 Masahiro Terabe , Takashi Washio , Hiroshi Motoda: S 3 Bagging: Fast Classifier Induction Method with Subsampling and Bagging. IDA 2001 : 177-186 Takayuki Ikeda , Takashi Washio , Hiroshi Motoda: Basket Analysis on Meningitis Data. JSAI Workshops 2001 : 516-524 Takuya Wada , Hiroshi Motoda, Takashi Washio : Knowledge Acquisition from Both Human Expert and Data. PAKDD 2001 : 550-561 Makoto Tsukada , Takashi Washio , Hiroshi Motoda: Automatic Web-Page Classification by Using Machine Learning Methods. Web Intelligence 2001 : 303-313 Takuya Wada , Tadashi Horiuchi , Hiroshi Motoda, Takashi Washio : A Description Length-Based Decision Criterion for Default Knowledge in the Ripple Down Rules Method. Knowl. Inf. Syst. 3 (2): 146-167 (2001) Takashi Matsuda , Tadashi Horiuchi , Hiroshi Motoda, Takashi Washio : Graph-Based Induction for General Graph Structured Data and Its Application to Chemical Compound Data. Discovery Science 2000 : 99-111 Takashi Washio , Hiroshi Motoda, Yuji Niwa : Enhancing the Plausibility of Law Equation Discovery. ICML 2000 : 1127-1134 Takashi Matsuda , Tadashi Horiuchi , Hiroshi Motoda, Takashi Washio : Extension of Graph-Based Induction for General Graph Structured Data. PAKDD 2000 : 420-431 Manoranjan Dash , Huan Liu , Hiroshi Motoda: Consistency Based Feature Selection. PAKDD 2000 : 98-109 Akihiro Inokuchi , Takashi Washio , Hiroshi Motoda: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. PKDD 2000 : 13-23 Hiroshi Motoda, Setsuo Arikawa : Special Feature on Discovery Science. New Generation Comput. 18 (1): 13-16 (2000) Manoranjan Dash , Huan Liu , Hiroshi Motoda: Feature Selection Using Consistency Measure. Discovery Science 1999 : 319-320 Akihiro Inokuchi , Takashi Washio , Hiroshi Motoda: Derivation of the Topology Structure from Massive Graph Data. Discovery Science 1999 : 330-332 Takashi Matsuda , Tadashi Horiuchi , Hiroshi Motoda, Takashi Washio , Kohei Kumazawa , Naohide Arai : Graph-Based Induction for General Graph Structured Data. Discovery Science 1999 : 340-342 Takashi Washio , Hiroshi Motoda, Niwa Yuji : Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains. IJCAI 1999 : 772-779 Masahiro Terabe , Osamu Katai , Tetsuo Sawaragi , Takashi Washio , Hiroshi Motoda: A Data Pre-processing Method Using Association Rules of Attributes for Improving Decision Tree. PAKDD 1999 : 143-147 Hiroshi Motoda: Computer Assisted Discovery of First Principle Equations from Numeric Data (Abstract). PAKDD 1999 : 2 Takuya Wada , Tadashi Horiuchi , Hiroshi Motoda, Takashi Washio : Characterization of Default Knowledge in Ripple Down Rules Method. PAKDD 1999 : 284-295 Akihiro Inokuchi , Takashi Washio , Hiroshi Motoda, Kouhei Kumasawa , Naohide Arai : Basket Analysis for Graph Structured Data. PAKDD 1999 : 420-431 Hing-Yan Lee , Hiroshi Motoda: PRICAI'98, Topics in Artificial Intelligence, 5th Pacific Rim International Conference on Artificial Intelligence, Singapore, November 22-27, 1998, Proceedings Springer 1998 Setsuo Arikawa , Hiroshi Motoda: Discovery Science, First International Conference, DS '98, Fukuoka, Japan, December 14-16, 1998, Proceedings Springer 1998 Takashi Washio , Hiroshi Motoda: Discovering Admissible Simultaneous Equations of Large Scale Systems. AAAI/IAAI 1998 : 189-196 Takashi Washio , Hiroshi Motoda: Development of SDS2: Smart Discovery System for Simultaneous Equation Systems. Discovery Science 1998 : 352-363 Huan Liu , Hiroshi Motoda, Manoranjan Dash : A Monotonic Measure for Optimal Feature Selection. ECML 1998 : 101-106 Takashi Washio , Hiroshi Motoda: Mining Association Rules for Estimation and Prediction. PAKDD 1998 : 417-419 Hiroshi Motoda, Kenichi Yoshida : Machine Learning Techniques to Make Computers Easier to Use. Artif. Intell. 103 (1-2): 295-321 (1998) Huan Liu , Hiroshi Motoda: Guest Editors' Introduction: Feature Transformation and Subset Selection. IEEE Intelligent Systems 13 (2): 26-28 (1998) Hiroshi Motoda, Kenichi Yoshida : Machine Learning Techniques to Make Computers Easier to Use. IJCAI 1997 : 1622-1631 Takashi Washio , Hiroshi Motoda: Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints. IJCAI (2) 1997 : 810-819 Byeong Ho Kang , Kenichi Yoshida , Hiroshi Motoda, Paul Compton : Help Desk System with Intelligent Interface. Applied Artificial Intelligence 11 (7-8): 611-631 (1997) Takashi Washio , Hiroshi Motoda: A History-Oriented Envisioning Method. PRICAI 1996 : 312-323 Shingo Nishioka , Atsuo Kawaguchi , Hiroshi Motoda: Process Labeled Kernel Profiling: A New Facility to Profile System Activities. USENIX Annual Technical Conference 1996 : 295-306 Kenichi Yoshida , Hiroshi Motoda: Tables, Graphs and Logic for Induction. Machine Intelligence 15 1995 : 298-311 Atsuo Kawaguchi , Shingo Nishioka , Hiroshi Motoda: A Flash-Memory Based File System. USENIX Winter 1995 : 155-164 Kenichi Yoshida , Hiroshi Motoda: CLIP: Concept Learning from Inference Patterns. Artif. Intell. 75 (1): 63-92 (1995) Riichiro Mizoguchi , Hiroshi Motoda: Expert Systems Research in Japan. IEEE Expert 10 (3): 14-23 (1995) N. Hari Narayanan , Masaki Suwa , Hiroshi Motoda: How Things Appear to Work: Predicting Behaviors from Device Diagrams. AAAI 1994 : 1161-1167 Masaki Suwa , Hiroshi Motoda: Learning Perceptually Chunked Macro Operators. Machine Intelligence 13 1994 : 419-440 Masaki Sssuwa , Hiroshi Motoda: PCLEARN: A Computer Model for Learning Perceptual Chunks. AI Commun. 7 (2): 114-125 (1994) Makoto Iwayama , Nitin Indurkhya , Hiroshi Motoda: A New Algorithm for Automatic Configuration of Hidden Markov Models. ALT 1993 : 237-250 Kenichi Yoshida , Hiroshi Motoda, Nitin Indurkhya : Unifying Learning Methods by Colored Digraphs. ALT 1993 : 342-355 Masaki Suwa , Hiroshi Motoda: A Perceptual Criterion for Visually Controlling Learning. ALT 1993 : 356-369 Masaki Suwa , Hiroshi Motoda: On dealing with dynamic utility of learned knowledge. Machine Intelligence 14 1993 : 113- Hiroshi Motoda, Riichiro Mizoguchi , John H. Boose , Brian R. Gaines : Knowledge Acquisition for Knowledge-Based Systems. IEEE Expert 6 (4): 53-64 (1991) Atsuo Kawaguchi , Hiroshi Motoda, Riichiro Mizoguchi : Interview-Based Knowledge Acquisition Using Dynamic Analysis. IEEE Expert 6 (5): 47-60 (1991) Hiroshi Motoda: The Current Status of Expert System Development and Related Technologies in Japan. IEEE Expert 5 (4): 3-11 (1990) Akito Sakurai , Hiroshi Motoda: Proving Definite Clauses without Explicit Use of Inductions. LP 1988 : 11-26 Hiroshi Motoda, Naoyuki Yamada , Kenichi Yoshida : A Knowledge based System for Plant Diagnosis. FGCS 1984 : 582-588 Naoyuki Yamada , Hiroshi Motoda: A Diagnosis Method of Dynamic System Using the Knowledge on System Description. IJCAI 1983 : 225-229 1 [ 65 ] [ 73 ] [ 76 ] 2 [ 68 ] 3 [ 31 ] [ 36 ] 4 [ 29 ] [ 39 ] [ 60 ] 5 [ 74 ] 6 [ 6 ] 7 [ 20 ] 8 [ 26 ] [ 38 ] [ 41 ] [ 64 ] 9 [ 67 ] [ 68 ] 10 [ 73 ] [ 76 ] 11 [ 65 ] [ 75 ] 12 [ 55 ] 13 [ 60 ] 14 [ 6 ] 15 [ 63 ] [ 66 ] 16 [ 65 ] 17 [ 68 ] 18 [ 73 ] [ 76 ] 19 [ 32 ] [ 36 ] [ 42 ] [ 44 ] [ 45 ] 20 [ 48 ] 21 [ 9 ] [ 10 ] 22 [ 31 ] [ 37 ] [ 40 ] [ 62 ] 23 [ 10 ] 24 [ 20 ] 25 [ 34 ] [ 51 ] 26 [ 5 ] [ 16 ] [ 18 ] 27 [ 31 ] 28 [ 36 ] 29 [ 68 ] 30 [ 67 ] [ 68 ] 31 [ 30 ] 32 [ 23 ] [ 26 ] [ 38 ] [ 41 ] [ 52 ] [ 57 ] [ 64 ] [ 69 ] [ 71 ] 33 [ 71 ] 34 [ 36 ] [ 42 ] [ 44 ] [ 54 ] [ 59 ] [ 63 ] [ 66 ] 35 [ 72 ] 36 [ 5 ] [ 6 ] [ 14 ] 37 [ 60 ] 38 [ 73 ] [ 76 ] 39 [ 13 ] 40 [ 70 ] [ 77 ] 41 [ 16 ] [ 18 ] 42 [ 43 ] [ 50 ] 43 [ 70 ] [ 74 ] [ 77 ] 44 [ 74 ] 45 [ 3 ] 46 [ 34 ] [ 51 ] 47 [ 11 ] 48 [ 7 ] [ 8 ] [ 12 ] [ 13 ] 49 [ 34 ] [ 49 ] [ 51 ] 50 [ 46 ] 51 [ 32 ] [ 45 ] [ 47 ] [ 56 ] 52 [ 19 ] [ 21 ] [ 25 ] [ 27 ] [ 28 ] [ 31 ] [ 32 ] [ 34 ] [ 35 ] [ 36 ] [ 37 ] [ 40 ] [ 42 ] [ 43 ] [ 44 ] [ 45 ] [ 46 ] [ 47 ] [ 48 ] [ 49 ] [ 50 ] [ 51 ] [ 53 ] [ 54 ] [ 55 ] [ 56 ] [ 58 ] [ 59 ] [ 61 ] [ 62 ] [ 63 ] [ 65 ] [ 66 ] [ 70 ] [ 72 ] [ 73 ] [ 75 ] [ 76 ] [ 77 ] 53 [ 72 ] 54 [ 1 ] [ 2 ] 55 [ 73 ] [ 76 ] 56 [ 2 ] [ 9 ] [ 15 ] [ 17 ] [ 20 ] [ 22 ] [ 24 ] [ 73 ] [ 76 ] 57 [ 54 ] [ 55 ] [ 56 ] [ 58 ] [ 59 ] [ 63 ] [ 66 ] 58 [ 57 ] [ 64 ] [ 69 ] 59 [ 35 ]  ©2005 Association for Computing Machinery |
