Papers - Kudo Yasuo

21. Epistemic Logic Founded on Nonignorance

    Seiki Akama, Tetsuya Murai and Yasuo Kudo，International Journal of Intelligent Systems，vol.28，(9)，(p.883 ～ 891)，2013.09

    

22. 主体的学習を促す「与えない」演習の実践

    佐藤和彦，倉重健太郎，寺本渉，工藤康生，佐賀聡人，工学教育，vol.61，(3)，(p.56 ～ 61)，2013.06

    

23. A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables

    Yasuo Kudo and Tetsuya Murai，Journal of Advanced Computational Intelligence and Intelligent Informatics，vol.17，(3)，(p.371 ～ 376)，2013.05

    

24. A Revised Approach to Solving the Symbolic Value Partition Problem from a Viewpoint of Roughness of Partitions

    Yasuo Kudo，Int. J. Reasoning-based Intelligent Systems，vol.4，(5)，(p.129 ～ 139)，2012.11

    

25. Study on Feature Extraction Method for Product Design --- Proposal for Method of Inferring Decisoin Classes by Variable Precision Rough Sets Models and Principle Componet Analysis

    関口彰・井上勝雄・酒井祐輔・工藤康生，Bulletin of Japanese Society for the Science of Design，vol.58，(3)，(p.59 ～ 68)，2011.12

    

26. A heuristic method for discovering biomarker candidates based on rough set theory

    Yasuo Kudo and Yoshifumi Okada，Bioinformation，vol.6，(5)，(p.200 ～ 203)，2011.06

    

27. A sequential pattern mining algorithm using rough set theory

    Ken Kaneiwa and Yasuo Kudo，International Journal of Approximate Reasoning，vol.52，(p.881 ～ )，2011.04

    

28. Autonomous agent control based on variable neighbourhoods

    Seiki Ubukata, Yasuo Kudo, and Tetsuya Murai，International Journal of Reasoning-based Intelligent Systems，vol.3，(1)，(p.8 ～ 13)，2011.01

    

29. Heuristic Algorithm for Attribute Reduction Based on Classification Ability by Condition Attributes

    Yasuo Kudo and Tetsuya Murai，Journal of Advanced Computational Intelligence and Intelligent Infomatics，vol.15，(1)，(p.102 ～ 109)，2011.01

    

30. An Evaluation Method of Relative Reducts Based on Roughness of Partitions

    Yasuo Kudo and Tetsuya Murai，International Journal of Cognitive Informatics and Natural intelligence，vol.4，(2)，(p.50 ～ 62)，2010.06

    

31. Network Inversion for the Cocktail-Data Quantification using Distance Mapping Learning Networks

    奥谷 勝行, 塩谷 浩之, 工藤 康生, 沖井 廣宣，Journal of Japan Society of Kansei Engineering，vol.9，(2)，(p.431 ～ 437)，2010.02

    

32. An Application of Rough Set Analysis to a Psycho-Physiological Study - Assessing the Relation Between Psychological Scale and Immunological Biomarker

    Shusaku Nomura and Yasuo Kudo，Journal of Advanced Computational Intelligence and Intelligent Informatics，vol.13，(4)，(p.352 ～ 359)，2009.07

    

33. A Granularity-Based Framework of Deduction, Induction and Abduction

    Yasuo Kudo, Tetsuya Murai and Seiki Akama，International Journal of Approximate Reasoning，vol.50，(8)，(p.1215 ～ 1226)，2009.06

    

34. A Modal Characterization of Visibility and Focus in Granular Reasoning

    Yasuo Kudo and Tetsuya Murai，Journalof Advanced Computational Intelligence and Intelligent Infomatics，vol.13，(3)，(p.297 ～ 303)，2009.05

    

35. Missing Value Semantics and Absent Value Semantics for Incomplete Information in Object-Oriented Rough Set Models

    Yasuo Kudo and Tetsuya Murai，Granular Computing: At the Junction ofRough Sets and Fuzzy Sets，(p.3 ～ 21)，2008

    

36. A Role of Granularity and Background Knowledge in Reasoning Processes

    Tetsuya Murai, Yasuo Kudo and Seiki Akama，Kansei Engineering International，vol.6，(3)，(p.41 ～ 46)，2006.11

    

37. A Simple Recommendation System Based on Rough Set Theory

    Yasuo Kudo, Shohei Amano, Takahiro Seino and Tetsuya Murai，Kansei Engineering International，vol.6，(3)，(p.19 ～ 24)，2006.11

    

38. A Theoretical Formulation of Object--Oriented Rough Set Models

    Yasuo Kudo and Tetsuya Murai，Journal of Advanced Computational Intelligence and Intelligent Informatics，vol.10，(5)，(p.612 ～ 620)，2006.09

    

39. A Family of Polymodal Systems and Its Application to Generalized Possibility Measures and Multi-Rough Sets

    Sadaaki Miyamoto, Tetsuya Murai and Yasuo Kudo，Journal of AdvancedComputational Intelligence and Intelligent Informatics，vol.10，(5)，(p.625 ～ 632)，2006.09

    

40. Image Retrieval of Large Intestine Images Using Feature Selection Agents

    沖井広宣・工藤康生・寺島賢紀，パーソナルコンピュータユーザ利用技術協会論文誌，vol.16，(3)，(p.53 ～ 60)，2006.03