A neural computing approach to graph partitioning problems ニューラルコンピューティングによるグラフ分割アルゴリズムの研究
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著者
書誌事項
- タイトル
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A neural computing approach to graph partitioning problems
- タイトル別名
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ニューラルコンピューティングによるグラフ分割アルゴリズムの研究
- 著者名
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齊藤, 孝之
- 著者別名
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サイトウ, タカユキ
- 学位授与大学
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慶応義塾大学
- 取得学位
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博士 (政策・メディア)
- 学位授与番号
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甲第1782号
- 学位授与年月日
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2000-03-23
注記・抄録
博士論文
目次
- 論文目録 / (0001.jp2)
- 博士論文要旨 / (0005.jp2)
- Contents / p2 (0008.jp2)
- List of Tables / p4 (0010.jp2)
- List of Figures / p6 (0012.jp2)
- 1 Introduction / p1 (0014.jp2)
- 1.1 Graph Partitioning Problems / p1 (0014.jp2)
- 1.2 Algorithms for Combinatorial Optimization Problems / p2 (0015.jp2)
- 1.3 Objectives of this Research / p4 (0017.jp2)
- 1.4 Overview of m-Way Graph Partitioning Problem / p4 (0017.jp2)
- 1.5 Overview of Political Districting Problem / p7 (0020.jp2)
- 2 Neural Networks for Optimization Problems / p13 (0026.jp2)
- 2.1 Basic Components of Artificial Neural Network / p13 (0026.jp2)
- 2.2 Neuron Models / p13 (0026.jp2)
- 2.3 Brief History of Artificial Neural Networks / p16 (0029.jp2)
- 2.4 Neural Network Models for Combinatorial Optimization Problems / p17 (0030.jp2)
- 2.5 Recurrent Neural Network Model / p20 (0033.jp2)
- 2.6 Recurrent Neural Networks and Graph Problems / p21 (0034.jp2)
- 3 m-Way Graph Partitioning Problem / p23 (0036.jp2)
- 3.1 Problem Formulation / p23 (0036.jp2)
- 3.2 Neuron Model and Neural Representation / p25 (0038.jp2)
- 3.3 Motion Equation / p26 (0039.jp2)
- 3.4 Algorithm / p28 (0041.jp2)
- 3.5 Experimental Results / p31 (0044.jp2)
- 3.6 Discussion / p37 (0050.jp2)
- 4 Political Districting Problem / p41 (0054.jp2)
- 4.1 Problem Formulation / p41 (0054.jp2)
- 4.2 Neuron Model / p42 (0055.jp2)
- 4.3 Neural Representation / p43 (0056.jp2)
- 4.4 Motion Equation / p43 (0056.jp2)
- 4.5 Algorithm / p47 (0060.jp2)
- 4.6 Simulations / p50 (0063.jp2)
- 4.7 Discussion / p71 (0084.jp2)
- 5 Conclusions and Future Works / p78 (0091.jp2)
- 5.1 Conclusions / p78 (0091.jp2)
- 5.2 Future Works / p78 (0091.jp2)
- References / p81 (0094.jp2)
- A Convergence Theorem and Proof of Recurrent Neural Network / p89 (0102.jp2)
- A.1 Hysteresis McCulloch-Pitts neural network / p89 (0102.jp2)
- A.2 Maximum Neural Network Convergence / p91 (0104.jp2)
- B Political Districting Problem / p93 (0106.jp2)
- B.1 Input Data for 46-Prefecture Districting Problem / p93 (0106.jp2)
- B.2 Input Data for Senate Districting Problem / p109 (0122.jp2)
- B.3 Simulation Results for 46-Prefecture Districting Problem / p119 (0132.jp2)
- B.4 Simulation Results for Senate Districting Problem / p125 (0138.jp2)
- ニューラルコンピューティングによる小選挙区区割り手法 / p588 (0174.jp2)
- Neural Computing for the m-Way Graph Partitioning Problem / p942 (0186.jp2)