The automatic generation of expert systems using machine learning コンピュータ学習による知識習得ツールに関する基礎的研究

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著者

    • Ross Peter Clement ロス ピーター クレメント

書誌事項

タイトル

The automatic generation of expert systems using machine learning

タイトル別名

コンピュータ学習による知識習得ツールに関する基礎的研究

著者名

Ross Peter Clement

著者別名

ロス ピーター クレメント

学位授与大学

豊橋技術科学大学

取得学位

工学博士

学位授与番号

甲第47号

学位授与年月日

1991-03-31

注記・抄録

博士論文

目次

  1. CONTENTS / p8 (0009.jp2)
  2. ABSTRACT / p2 (0003.jp2)
  3. JAPANESE ABSTRACT / p4 (0005.jp2)
  4. ACKNOWLEDGEMENTS / p6 (0007.jp2)
  5. QUOTE / p7 (0008.jp2)
  6. CONTENTS / p8 (0009.jp2)
  7. INTRODUCTION TO EXPERT SYSTEMS AND MACHINE LEARNING / I-1 / (0014.jp2)
  8. 1 EXPERT SYSTEMS CONCEPTS / I-1 / (0014.jp2)
  9. 1.1 EXPERT SYSTEMS AS A PROGRAMMING METHODOLOGY / I-1 / (0014.jp2)
  10. 1.2 RULES / I-2 / (0015.jp2)
  11. 1.3 THE REASONING ENGINE / I-5 / (0018.jp2)
  12. 1.4 ADVANTAGES OF EXPERT SYSTEMS / I-7 / (0020.jp2)
  13. 1.5 PROBLEMS IN CREATING EXPERT SYSTEMS / I-7 / (0020.jp2)
  14. 1.6 ALTERNATIVE METHODS FOR CREATING EXPERT SYSTEMS / I-9 / (0022.jp2)
  15. 2 MACHINE LEARNING CONCEPTS / I-10 / (0023.jp2)
  16. 2.1 LEARNING FROM EXAMPLES / I-10 / (0023.jp2)
  17. 2.2 LEARNING EXPERT SYSTEMS FROM TRAINING INSTANCES / I-12 / (0025.jp2)
  18. 2.3 INDUCTIVE LEARNING ALGORITHMS / I-17 / (0030.jp2)
  19. 3 FINAL WORD / I-22 / (0035.jp2)
  20. CHAPTER1:INTRODUCTION TO THIS THESIS / p1 (0036.jp2)
  21. 1.1 INTRODUCTION / p1 (0036.jp2)
  22. 1.2 KNOWLEDGE ACQUISITION / p1 (0036.jp2)
  23. 1.3 KNOWLEDGE ACQUISITION USING MACHINE LEARNING / p3 (0038.jp2)
  24. 1.4 THE KA PROCESS USING ML / p4 (0039.jp2)
  25. 1.5 PROBLEMS WITH CURRENT TECHNOLOGY / p5 (0040.jp2)
  26. 1.6 NEW TECHNOLOGY / p7 (0042.jp2)
  27. 1.7 OVERVIEW OF THIS THESIS / p8 (0043.jp2)
  28. CHAPTER2:THE BAMBOO ALGORITHM AND PARALLEL GENERALISATION / p9 (0044.jp2)
  29. 2.1 MACHINE LEARNING FOR REAL PROBLEMS / p9 (0044.jp2)
  30. 2.2 THE INDUCTION OF DECISION TREES / p10 (0045.jp2)
  31. 2.3 THE BAMBOO ALGORITHM / p15 (0050.jp2)
  32. 2.4 A COMPARISON OF BAMBOO AND C4 / p16 (0051.jp2)
  33. 2.5 PARALLEL GENERALISATION / p18 (0053.jp2)
  34. 2.6 THE SOYBEAN EXPERIMENT / p21 (0056.jp2)
  35. 2.7 THE ARTIFICIAL DATA EXPERIMENT / p22 (0057.jp2)
  36. 2.8 INADEQUACIES OF THE PROGRAM / p23 (0058.jp2)
  37. 2.9 CONCLUSIONS / p23 (0058.jp2)
  38. CHAPTER3:PARALLEL AND SEQUENTIAL LAYERING OF LEARNING STRATEGIES WITHIN A SINGLE PROGRAM / p25 (0060.jp2)
  39. 3.1 INTRODUCTION / p25 (0060.jp2)
  40. 3.2 PARALLEL AND SEQUENTIAL LAYERED LEARNING / p30 (0065.jp2)
  41. 3.3 IMPLEMENTATION / p33 (0068.jp2)
  42. 3.4 EXPERIMENTATION AND RESULTS / p47 (0082.jp2)
  43. 3.5 CONCLUSIONS / p51 (0086.jp2)
  44. 3.6 FUTURE WORK / p51 (0086.jp2)
  45. CHAPTER4:AUTOMATIC KNOWLEDGE ACQUISITION FOR NON-CLASSIFICATION EXPERT SYSTEMS / p53 (0088.jp2)
  46. 4.1 INTRODUCTION / p53 (0088.jp2)
  47. 4.2 REEXAMINING CLASSIFIER SYSTEMS / p54 (0089.jp2)
  48. 4.3 MULTIPLE CLASSIFICATION / p55 (0090.jp2)
  49. 4.4 SYSTEMS THAT CHANGE OVER TIME / p58 (0093.jp2)
  50. 4.5 FUNCTIONAL GENERALISATION / p61 (0096.jp2)
  51. 4.6 IMPLEMENTATION AND RESULTS / p63 (0098.jp2)
  52. 4.7 CONCLUSIONS / p67 (0102.jp2)
  53. 4.8 FUTURE WORK / p68 (0103.jp2)
  54. CHAPTER5:HOW TO MAKE AN EXPERT SYSTEM THAT DOESNT HAVE ANY RULES / p69 (0104.jp2)
  55. 5.1 INTRODUCTION / p69 (0104.jp2)
  56. 5.2 BACKGROUND / p71 (0106.jp2)
  57. 5.3 BAMBOO SHELL / p72 (0107.jp2)
  58. 5.4 CONCLUSIONS / p84 (0119.jp2)
  59. 5.5 FUTURE WORK / p85 (0120.jp2)
  60. CHAPTER6:THE AUTOMATIC GENERATION OF UNCERTAIN REASONING EXPERT SYSTEMS / p85 (0121.jp2)
  61. 6.1 NON-DETERMINISTIC DOMAINS / p85 (0121.jp2)
  62. 6.2 HUMAN REASONING IN NON-DETERMINISTIC DOMAINS / p86 (0122.jp2)
  63. 6.3 UNCERTAIN REASONING EXPERT SYSTEMS / p87 (0123.jp2)
  64. 6.4 GENERATING UNCERTAIN REASONING EXPERT SYSTEMS / p89 (0125.jp2)
  65. 6.5 NEW HEURISTICS FOR UNCERTAIN REASONING / p94 (0130.jp2)
  66. 6.6 THE SELECT PROCEDURE / p99 (0135.jp2)
  67. 6.7 CONCLUSIONS / p99 (0135.jp2)
  68. CHAPTER7:LEARNING EXPERT SYSTEMS BY BEING CORRECTED / p101 (0137.jp2)
  69. 7.1 INTRODUCTION / p101 (0137.jp2)
  70. 7.2 LEARNING FROM EXAMPLES / p103 (0139.jp2)
  71. 7.3 LEARNING FROM GENERAL/SPECIFIC INFORMATION / p105 (0141.jp2)
  72. 7.4 KABCO:LEARNING BY BEING CORRECTED / p107 (0143.jp2)
  73. 7.5 KABCO AND MACHINE LEARNING PROGRAMS / p118 (0154.jp2)
  74. 7.6 KABCO AND KNOWLEDGE EDITORS / p119 (0155.jp2)
  75. 7.7 ACTUAL USE OF KABCO / p120 (0156.jp2)
  76. 7.8 LIMITATIONS OF KABCO / p123 (0159.jp2)
  77. 7.9 FUTURE WORK / p125 (0161.jp2)
  78. 7.10 CONCLUSIONS / p127 (0163.jp2)
  79. CHAPTER8:SUMMARY,CONCLUSIONS,& FUTURE WORK / p128 (0164.jp2)
  80. 8.1 SUMMARY / p128 (0164.jp2)
  81. 8.2 CONCLUSIONS / p129 (0165.jp2)
  82. 8.3 FUTURE WORK / p130 (0166.jp2)
  83. 8.4 FINAL WORD / p133 (0169.jp2)
  84. BIBLIOGRAPHY / (0170.jp2)
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各種コード

  • NII論文ID(NAID)
    500000078735
  • NII著者ID(NRID)
    • 8000000078939
  • DOI(NDL)
  • NDL書誌ID
    • 000000243049
  • データ提供元
    • NDL-OPAC
    • NDLデジタルコレクション
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